The proliferation of hidden WiFi cameras has raised serious privacy concerns, making their accurate detection and localization essential for the secure development of future intelligent wireless networks. However, existing solutions often require substantial user involvement, large movement spaces, predefined system parameters, or pre-collected training data, limiting their practicality and scalability. In this paper, we present DiffLoc+, a novel and low-cost system that localizes hidden WiFi cameras by harnessing the fundamental physical principle of electromagnetic diffraction. When an obstacle crosses the line-of-sight path between a transmitter and a receiver, it causes a distinctive signal attenuation pattern. We theoretically analyze the feasibility of exploiting this phenomenon for localization and identify two key conditions for building an unbiased diffraction-based model: symmetry and observability. To satisfy these conditions, DiffLoc+ introduces a controllable diffraction generation mechanism that precisely rotates a small metal plate around a WiFi receiver (e.g. a Raspberry Pi), producing a stable and predictable diffraction “shadowing” effect. We then construct an unbiased localization model that maps this effect to the azimuth of the camera. To ensure the robustness of the theoretical model in real-world applications, DiffLoc+ further introduces two robustness-enhancing mechanisms: (1) an attenuation-region difference-driven subcarrier selection method, which filters subcarriers that reliably reflect the diffraction attenuation pattern by quantifying the signal contrast between diffraction- and reflection-dominated regions; and (2) an uncertainty evaluation framework that integrates result consistency and diffraction signal quality to eliminate unreliable estimates. Implemented entirely with commodity off-the-shelf (COTS) hardware, DiffLoc+ achieves an average angular error of 11.92° across six diverse indoor environments and eleven commercial camera models, demonstrating its effectiveness and robustness.

Unauthorized editing of speech recordings poses a significant threat to the security and authenticity of speeches, particularly in the forensic and legal fields. Even worse, the speech is increasingly at risk of being tampered with due to the development of AI techniques (e.g., Audio Deepfake). It is difficult for normal users to guarantee what they say has not been illegally changed. Audio watermark techniques are recognized as an active method against speech forgery. However, such techniques suffer from audio quality degradation and non-real-time insertion. Therefore, they cannot be adopted into real-time voice applications against forgery on remote recordings, e.g., phone calls, live broadcasts, and online meetings. Fortunately, high-definition (HD) audio techniques provide ultrasonic bands without distortion. Therefore, ultrasonic creditable factors can be utilized. We propose an audio tamper-proof system, named Aegis. It provides commodity mobile devices (e.g., smartphones) with an effective method of real-time insertion of inaudible creditable factors. Users can claim that audio with no or mismatched ultrasound is invalid and illegal. In particular, we explore a novel acoustic nonlinear phenomenon where audible signals can be modulated onto the ultrasonic spectrum. By emphasizing the correlation between speech signals and ultrasound, we realize effective defense against various tampering methods. Extensive evaluations demonstrate that Aegis yields a detection accuracy of 99.5% on average even against unseen tampering methods.

Continuous authentication is increasingly critical for cyber security. However, existing approaches are time-consuming due to their simplistic signal modulation and low efficiency in feature extraction. In this paper, we propose a continuous authentication technique, OnePiece. OnePiece is free from the requirement of in-ear microphones, which are necessary for existing earphone authentication systems. It exploits out-ear microphones for biometrics extraction, which are ubiquitous on off-the-shelf earphones. We analyze the acoustic response model of ears towards out-ear microphones via the air, which is different from that towards in-ear microphones. A frequency-varying ultrasonic modulation scheme is proposed to characterize in-depth ear biometrics in user-friendly, error-free, and time-efficient ways. Therefore, OnePiece enables quick-pass authentication once users wear the earphones, followed by continuous authentication covering the whole course. Moreover, we propose a wake-up mechanism to reduce the consumed power, which addresses the key power consumption issue in ultrasonic sensing techniques. Particularly, OnePiece can be smoothly deployed on off-the-shelf wired and wireless earphones. It performs good cross-device performance in which users just register only once. Extensive evaluations are conducted to validate its effectiveness under real-world scenarios.

Modern communication tools often suffer quality issues from background noise and other speakers. Existing solutions either face mask blockages or need specialized gear, limiting use on standard earphones. Vibration-based methods, focusing on below 8 kHz speech, lack high frequencies, reducing naturalness. To solve these, we present an innovative multi-sensory speech enhancement framework for commercial earphones. It uses built-in inertial measurement units (IMUs) even with ultra-low 25 Hz sampling as extra input. We developed a mathematical framework linking IMU movement data and vocal signals, incorporating unsupervised domain adaptation to reduce individual differences and guiding intermediate-data integration to connect limited-frequency IMU info with 22 kHz full-range speech.

WiFi based respiratory detection has attracted increasing attentions due to its ubiquity and convenience. In Non-Line-of-Sight (NLoS) scenarios, WiFi signals reflected from human target are blocked by obstacles and become much weaker, thus limiting the sensing range and hindering the practical deployment. The existing best respiratory detection system extended the sensing range by scaling and aligning dynamic components in WiFi signals. However, its dynamic component scaling causes the amplification of noise, while its dynamic component alignment increases computation complexity due to the traversal on all possible rotation angles. To address the above issues, in this paper we first build WiFi sensing range models for respiratory detection in NLoS scenario, find factors that limit the sensing range, and then propose a new respiratory detection system named RaliSense which can further rapidly extend the sensing range in NLoS scenario. The main idea of RaliSense is rapidly aligning dynamic components without amplifying noise, based on change direction vector and CSI ratio sum polarity of dynamic components. The proposed change direction vector is obtained by calculating the direction on which the noisy dynamic components have the maximum variance, and CSI ratio sum polarity is then obtained by summing the dynamic components which have been rotated by the change direction vector. According to the CSI ratio sum polarity, the rotation angle is quickly adjusted for aligning dynamic components. Extensive simulation and experiment results verify the effectiveness of our proposed sensing range models. The results also demonstrate that our proposed system RaliSense can effectively extend sensing range in NLoS scenario, achieving a 22.7% improvement over the best existing work but spending only a quarter of its computation time.

Continuous authentication is increasingly critical for cyber security. However, existing approaches are time-inefficient due to their simple signal modulation with low-effective feature extraction throughput. In this paper, we propose a continuous authentication technique, OnePiece. OnePiece is free from the requirement of in-ear microphones, which are necessary for existing earphone authentication systems. It exploits out-ear microphones for biometrics extraction, which are ubiquitous on off-the-shelf earphones. We analyze the acoustic response model of ears towards out-ear microphones via the air, which is different from that towards in-ear microphones. A frequency-varying ultrasonic modulation scheme is proposed to characterize in-depth ear biometrics in user-friendly, error-free, and time-efficient ways. Therefore, OnePiece enables quick-pass authentication once users wear the earphones, followed by continuous authentication covering the whole course. Moreover, we propose a wake-up mechanism to reduce the consumed power, which addresses the key power consumption issue in ultrasonic sensing techniques. Particularly, OnePiece can be smoothly deployed on off-the-shelf wired and wireless earphones. It performs good cross-device performance in which users just register only once. Extensive evaluations are conducted to validate its effectiveness under real-world scenarios.

Human-machine interactions (HMIs), e.g., touchscreens, are essential for users to interact with mobile devices. They are also beneficial in resisting emerging active attacks, which aim at maliciously controlling mobile devices, e.g., smartphones and tablets. With touchscreen-like HMIs, users can notice and interrupt malicious actions conducted by the attackers timely and perform necessary countermeasures, e.g., tapping the ‘Quit’ button on the touchscreen. However, the effect of HMI-oriented active attacks has not been investigated yet. In this paper, we present a practical attack towards touch-based devices, namely Expelliarmus. It reveals a new attack surface of active attacks for hijacking users’ operations and thus taking full control over victim devices. Expelliarmus neutralizes users’ touch commands by producing a reverse current via electromagnetic interference (EMI). Since the reverse current offsets the current change caused by a touch, the touchscreen detects no current change and thus ignores users’ commands. Besides this basic denial-of-service attack, we also realize a target cancellation attack, which can neutralize target commands, e.g., ‘Quit’ without interference in irrelevant operations. Thus, the active attack can be completely performed without interruption from users, even if they are alerted by the abnormal events. Extensive evaluations demonstrate the effectiveness of Expelliarmus on 29 off-the-shelf devices.

Covert eavesdropping via microphones has always been a major threat to user privacy. Benefiting from the acoustic non-linearity property, the ultrasonic microphone jammer (UMJ) is effective in resisting this long-standing attack. However, prior UMJ researches underestimate adversary's attacking capability in reality and miss critical metrics for a thorough evaluation. The strong assumptions of adversary unable to retrieve information under low word recognition rate, and adversary's weak denoising abilities in the threat model make these works overlook the vulnerability of existing UMJs. As a result, their UMJs' resilience is overestimated. In this paper, we refine the adversary model and completely investigate potential eavesdropping threats. Correspondingly, we define a total of 12 metrics that are necessary for evaluating UMJs' resilience. Using these metrics, we propose a comprehensive framework to quantify UMJs' practical resilience. It fully covers three perspectives that prior works ignored to some degree, i.e., ambient information, semantic comprehension, and collaborative recognition. Guided by this framework, we can thoroughly and quantitatively evaluate the resilience of existing UMJs towards eavesdroppers. Our extensive assessment results reveal that most existing UMJs are vulnerable to sophisticated adverse approaches. We further outline the key factors influencing jammers' performance and present constructive suggestions for UMJs' future designs.

In cyber-physical systems, inertial sensors are the basis for identifying motion states and making actuation decisions. However, extensive studies have proved the vulnerability of those sensors under acoustic transduction attacks, which leverage malicious acoustics to trigger sensor measurement errors. Unfortunately, the threat from such attacks is not assessed properly because of the incomplete investigation on the attack's potential, especially towards multiple-degree-of-freedom systems, e.g., drones. To thoroughly explore the threat of acoustic transduction attacks, we revisit the attack model and design a new yet practical acoustic modulation-based attack, named KITE. Such an attack enables stable and controllable injections, even under frequency offset based distortions that limit the effect of prior attacking approaches. KITE exploits the potential threat of transduction attacks without the need of strengthening attackers' abilities. Furthermore, we extend the attack surface to multiple-degree-of-freedom (MDOF) systems, which are more widely deployed but ignored by prior work. Our study also covers the scenario of attacking moving targets. By revealing the practical threat from acoustic transduction attacks, we appeal for both the attention to their harm and necessary countermeasures.

Human-machine interactions (HMIs), e.g., touchscreens, are essential for users to interact with mobile devices. They are also beneficial in resisting emerging active attacks, which aim at maliciously controlling mobile devices, e.g., smartphones and tablets. With touchscreen-like HMIs, users can notice and interrupt malicious actions conducted by the attackers timely and perform necessary countermeasures, e.g., tapping the ‘Quit’ button on the touchscreen. However, the effect of HMI-oriented active attacks has not been investigated yet. In this paper, we present a practical attack towards touch-based devices, namely Expelliarmus. It reveals a new attack surface of active attacks for hijacking users’ operations and thus taking full control over victim devices. Expelliarmus neutralizes users’ touch commands by producing a reverse current via electromagnetic interference (EMI). Since the reverse current offsets the current change caused by a touch, the touchscreen detects no current change and thus ignores users’ commands. Besides this basic denial-of-service attack, we also realize a target cancellation attack, which can neutralize target commands, e.g., ‘Quit’ without interference in irrelevant operations. Thus, the active attack can be completely performed without interruption from users, even if they are alerted by the abnormal events. Extensive evaluations demonstrate the effectiveness of Expelliarmus on 29 off-the-shelf devices.

Ultrasonic microphone jammers protect speech privacy from being eavesdropped by leveraging microphones' non-linearity. However, existing jammers merely introduce independent noises and are vulnerable to capable adversaries who adopt advanced denoising techniques. We propose a novel jammer, namely MicFrozen. It reduces the signal-to-noise ratio (SNR) at the adversary's microphone from two perspectives, i.e., cancelling speech signals and adding noises that are difficult to be removed. It effectively cancels out the protected speech signals at the adversary without compromising the delivery of the signal to the targeted individual. MicFrozen further adds coherent noises that are coupled with the speech signals to resist removal by the adversary. Extensive evaluations show that MicFrozen can cause a low SNR (-13.6 dB) at the adversary and up to 96.9% of speech signals are unrecognized at the adversary even if state-of-the-art denoising techniques are adopted by the adversary. Comprehensive experiments demonstrate the effectiveness of MicFrozen confronted by capable adversaries.

Eavesdropping via inertial measurement units (IMUs) has brought growing concerns over smartphone users’ privacy. In such attacks, adversaries utilize IMUs, including accelerometers and gyroscopes, which require zero permissions for access to acquire speeches. A common countermeasure is to limit sampling rates (within 200 Hz) to reduce overlap of vocal fundamental bands (85$\sim$255 Hz) and inertial measurements (0$\sim$100 Hz). Nevertheless, we observe that IMUs sampling below 200 Hz still record adequate speech-related information because of aliasing distortions. Accordingly, we propose a practical side-channel attack, namelyInertiEAR, to break the defense of sampling rate restriction on the zero-permission eavesdropping. It leverages accelerometers and gyroscopes jointly to eavesdrop on both top and bottom speakers in smartphones. We exploit coherence between responses of the built-in accelerometer and gyroscope using a mathematical model. The coherence allows precise segmentation without manual assistance. We also mitigate the impact of hardware diversity and achieve better device-independent performance than existing approaches that have to massively increase training data from different smartphones for a scalable network model. These two advantages re-enable zero-permission attacks but also extend the attacking surface and endangering degree to off-the-shelf smartphones.InertiEARachieves the recognition accuracy of 78.8% with the cross-device accuracy of up to 60.9% among 12 smartphones.

Incompatible protocols and electromagnetic interference obstruct the realization of an everything-connected Internet of Things (IoT) communication network. Our system, Deaf-Aid, utilizes a stealthy speaker-to-gyroscope channel to build robust communication. Compared with existing solutions adopting physical covert channels, Deaf-Aid is free from the limitations of manual receiver distinction, additional hardware, conditional placement, or physical contact. It exploits ultrasounds to force gyroscopes embedded in receivers to resonate, so as to convey information. We investigate the relationship among axes in a gyroscope to deal with frequency offset and support multi-channel communication. Meanwhile, receivers are identified automatically via device fingerprints consisting of diversity of gyroscopes’ resonant frequency ranges. Furthermore, we enable Deaf-Aid the capability of mobile communication, which is an essential demand for IoT devices. We address the challenge of recovering accurate signals from motion interference. Extensive evaluations, including that on the commercial off-the-shelf devices, demonstrate that Deaf-Aid yields 47 bps with BER below 1%. To our best knowledge, Deaf-Aid is the first work to enable stealthy mobile IoT communication based on inertial sensors.

Covert eavesdropping via microphones has always been a major threat to user privacy. Benefiting from the acoustic non-linearity property, the ultrasonic microphone jammer (UMJ) is effective in resisting this long-standing attack. However, prior UMJ researches underestimate adversary’s attacking capability in reality and miss critical metrics for a thorough evaluation. The strong assumptions of adversary unable to retrieve information under low word recognition rate, and adversary’s weak denoising abilities in the threat model make these works overlook the vulnerability of existing UMJs. As a result, their UMJs’ resilience is overestimated. In this paper, we refine the adversary model and completely investigate potential eavesdropping threats. Correspondingly, we define a total of 12 metrics that are necessary for evaluating UMJs’ resilience. Using these metrics, we propose a comprehensive framework to quantify UMJs’ practical resilience. It fully covers three perspectives that prior works ignored in some degree, i.e., ambient information, semantic comprehension, and collaborative recognition. Guided by this framework, we can thoroughly and quantitatively evaluate the resilience of existing UMJs towards eavesdroppers. Our extensive assessment results reveal that most existing UMJs are vulnerable to sophisticated adverse approaches. We further outline the key factors influencing jammers’ performance and present constructive suggestions for UMJs’ future designs.

IMU-based eavesdropping has brought growing concerns over smartphone users’ privacy. In such attacks, adversaries utilize IMUs that require zero permissions for access to acquire speeches. A common countermeasure is to limit sampling rates (within 200 Hz) to reduce overlap of vocal fundamental bands (85-255 Hz) and inertial measurements (0-100 Hz). Nevertheless, we experimentally observe that IMUs sampling below 200 Hz still record adequate speech-related information because of aliasing distortions. Accordingly, we propose a practical side-channel attack, InertiEAR, to break the defense of sampling rate restriction on the zero-permission eavesdropping. It leverages IMUs to eavesdrop on both top and bottom speakers in smartphones. In the InertiEAR design, we exploit coherence between responses of the built-in accelerometer and gyroscope and their hardware diversity using a mathematical model. The coherence allows precise segmentation without manual assistance. We also mitigate the impact of hardware diversity and achieve better device-independent performance than existing approaches that have to massively increase training data from different smartphones for a scalable network model. These two advantages re-enable zero-permission attacks but also extend the attacking surface and endangering degree to off-the-shelf smartphones. InertiEAR achieves a recognition accuracy of 78.8% with a cross-device accuracy of up to 49.8% among 12 smartphones.

In cyber-physical systems, inertial sensors are the basis for identifying motion states and making actuation decisions. However, extensive studies have proved the vulnerability of those sensors under acoustic transduction attacks, which leverage malicious acoustics to trigger sensor measurement errors. Unfortunately, the threat from such attacks is not assessed properly because of the incomplete investigation on the attack's potential, especially towards multiple-degree-of-freedom systems, e.g., drones. To thoroughly explore the threat of acoustic transduction attacks, we revisit the attack model and design a new yet practical acoustic modulation-based attack, named KITE. Such an attack enables stable and controllable injections, even under frequency offset based distortions that limit the effect of prior attacking approaches. KITE exploits the potential threat of transduction attacks without the need of strengthening attackers' abilities. Furthermore, we extend the attack surface to multiple-degree-of-freedom systems, which are more widely deployed but ignored by prior work. Our study also covers the scenario of attacking moving targets. By revealing the practical threat from acoustic transduction attacks, we appeal for both the attention to their harm and necessary countermeasures.

With the advance in automatic speech recognition, voice user interface has gained popularity recently. Since the COVID-19 pandemic, VUI is increasingly preferred in online communication due to its non-contact. Additionally, various ambient noise impedes the public applications of voice user interfaces due to the requirement of audio-only speech recognition methods for a high signal-to-noise ratio. In this paper, we present Wavoice, the first noise-resistant multi-modal speech recognition system that fuses two distinct voice sensing modalities, i.e., millimeter-wave (mmWave) signals and audio signals from a microphone, together. One key contribution is that we model the inherent correlation between mmWave and audio signals. Based on it, Wavoice facilitates the real-time noise-resistant voice activity detection and user targeting from multiple speakers. Furthermore, we elaborate on two novel modules into the neural attention mechanism for multi-modal signals fusion, and result in accurate speech recognition. Extensive experiments verify Wavoice's effectiveness under various conditions with the character recognition error rate below 1% in a range of 7 meters. Wavoice outperforms existing audio-only speech recognition methods with lower character error rate and word error rate. The evaluation in complex scenes validates the robustness of Wavoice.

The material point method (MPM) recently demonstrated its efficacy at simulating many materials and the coupling between them on a massive scale. However, in scenarios containing debris, MPM manifests more dissipation and numerical viscosity than traditional Lagrangian methods. We have two observations from carefully revisiting existing integration methods used in MPM. First, nearby particles would end up with smoothed velocities without recovering momentum for each particle during the particle-grid-particle transfers. Second, most existing integrators assume continuity in the entire domain and advect particles by directly interpolating the positions from deformed nodal positions, which would trap the particles and make them harder to separate. We propose an integration scheme that corrects particle positions at each time step. We demonstrate our method's effectiveness with several large-scale simulations involving brittle materials. Our approach effectively reduces diffusion and unphysical viscosity compared to traditional integrators.

Internet of Things (IoT) devices are hindered from communicating with their neighbors by incompatible protocols or electromagnetic interference. Existing solutions adopting physical covert channels have limitations in receiver distinction, additional hardware, conditional placement, or physical contact. Our system, Deaf-Aid, utilizes the stealthy speaker-to-gyroscope channel to build robust protocol-independent communication with automatic receiver identification. Deaf-Aid exploits ultrasonic signals at a frequency corresponding to the target receiver, forcing the gyroscope inside to resonate, so as to convey information. We probe the relationship among axes in a gyroscope to surmount frequency offset ingeniously and support multi-channel communication. Meanwhile, Deaf-Aid identifies the receivers automatically via device fingerprints constituted by the diversity of resonant frequency ranges. Furthermore, we entitle Deaf-Aid the capability of mobile communication which is an essential demand for IoT devices. We address the challenge of accurate signals recovery from motion interference. Extensive evaluations demonstrate that Deaf-Aid yields 47bps with BER lower than 1% under motion interference. To our best knowledge, Deaf-Aid is the first work to enable stealthy mobile IoT communication on the basis of inertial motion sensors.

We propose Hierarchical Optimization Time Integration (HOT) for efficient implicit timestepping of the material point method (MPM) irrespective of simulated materials and conditions. HOT is an MPM-specialized hierarchical optimization algorithm that solves nonlinear timestep problems for large-scale MPM systems near the CFL limit. HOT provides convergent simulations out of the box across widely varying materials and computational resolutions without parameter tuning. As an implicit MPM timestepper accelerated by a custom-designed Galerkin multigrid wrapped in a quasi-Newton solver, HOT is both highly parallelizable and robustly convergent. As we show in our analysis, HOT maintains consistent and efficient performance even as we grow stiffness, increase deformation, and vary materials over a wide range of finite strain, elastodynamic, and plastic examples. Through careful benchmark ablation studies, we compare the effectiveness of HOT against seemingly plausible alternative combinations of MPM with standard multigrid and other Newton-Krylov models. We show how these alternative designs result in severe issues and poor performance. In contrast, HOT outperforms existing state-of-the-art, heavily optimized implicit MPM codes with an up to 10× performance speedup across a wide range of challenging benchmark test simulations.

Simulating viscoelastic polymers and polymeric fluids requires a robust and accurate capture of elasticity and viscosity. The computation is known to become very challenging under large deformations and high viscosity. Drawing inspirations from return mapping based elastoplasticity treatment for granular materials, we present a finite strain integration scheme for general viscoelastic solids under arbitrarily large deformation and non-equilibrated flow. Our scheme is based on a predictor-corrector exponential mapping scheme on the principal strains from the deformation gradient, which closely resembles the conventional treatment for elastoplasticity and allows straightforward implementation into any existing constitutive models. We develop a new Material Point Method that is fully implicit on both elasticity and inelasticity using augmented Lagrangian optimization with various preconditioning strategies for highly efficient time integration. Our method not only handles viscoelasticity but also supports existing elastoplastic models including Drucker-Prager and von-Mises in a unified manner. We demonstrate the efficacy of our framework on various examples showing intricate and characteristic inelastic dynamics with competitive performance.

Simulation methods are rapidly advancing the accuracy, consistency and controllability of elastodynamic modeling and animation. Critical to these advances, we require efficient time step solvers that reliably solve all implicit time integration problems for elastica. While available time step solvers succeed admirably in some regimes, they become impractically slow, inaccurate, unstable, or even divergent in others --- as we show here. Towards addressing these needs we present the Decomposed Optimization Time Integrator (DOT), a new domain-decomposed optimization method for solving the per time step, nonlinear problems of implicit numerical time integration. DOT is especially suitable for large time step simulations of deformable bodies with nonlinear materials and high-speed dynamics. It is efficient, automated, and robust at large, fixed-size time steps, thus ensuring stable, continued progress of high-quality simulation output. Across a broad range of extreme and mild deformation dynamics, using frame-rate size time steps with widely varying object shapes and mesh resolutions, we show that DOT always converges to user-set tolerances, generally well-exceeding and always close to the best wall-clock times across all previous nonlinear time step solvers, irrespective of the deformation applied.

In this paper, we introduce BiMocq 2 , an unconditionally stable, pure Eulerianbased advection scheme to efficiently preserve the advection accuracy of all physical quantities for long-term fluid simulations. Our approach is built upon the method of characteristic mapping (MCM). Instead of the costly evaluation of the temporal characteristic integral, we evolve the mapping function itself by solving an advection equation for the mappings. Dual mesh characteristics (DMC) method is adopted to more accurately update the mapping. Furthermore, to avoid visual artifacts like instant blur and temporal inconsistency introduced by re-initialization, we introduce multi-level mapping and back and forth error compensation. We conduct comprehensive 2D and 3D benchmark experiments to compare against alternative advection schemes. In particular, for the vortical flow and level set experiments, our method outperforms almost all state-of-art hybrid schemes, including FLIP, PolyPic and Particle-Level-Set, at the cost of only two Semi-Lagrangian advections. Additionally, our method does not rely on the particle-grid transfer operations, leading to a highly parallelizable pipeline. As a result, more than 45× performance acceleration can be achieved via even a straightforward porting of the code from CPU to GPU.

We present two new approaches for animating dynamic fracture involving large elastoplastic deformation. In contrast to traditional mesh-based techniques, where sharp discontinuity is introduced to split the continuum at crack surfaces, our methods are based on Continuum Damage Mechanics (CDM) with a variational energy-based formulation for crack evolution. Our first approach formulates the resulting dynamic material damage evolution with a Ginzburg-Landau type phase-field equation and discretizes it with the Material Point Method (MPM), resulting in a coupled momentum/damage solver rooted in phase field fracture: PFF-MPM. Although our PFF-MPM approach achieves convincing fracture with or without plasticity, we also introduce a return mapping algorithm that can be analytically solved for a wide range of general non-associated plasticity models, achieving more than two times speedup over traditional iterative approaches. To demonstrate the efficacy of the algorithm, we also develop a Non-Associated Cam-Clay (NACC) plasticity model with a novel fracture-friendly hardening scheme. Our NACC plasticity paired with traditional MPM composes a second approach to dynamic fracture, as it produces a breadth of organic, brittle material fracture effects on its own. Though NACC and PFF can be combined, we focus on exploring their material effects separately. Both methods can be easily integrated into any existing MPM solver, enabling the simulation of various fracturing materials with extremely high visual fidelity while requiring little additional computational overhead.

In this paper, we present a mixed explicit and semi-implicit Material Point Method for simulating particle-laden flows. We develop a Multigrid Preconditioned fluid solver for the Locally Averaged Navier Stokes equation. This is discretized purely on a semi-staggered standard MPM grid. Sedimentation is modeled with the Drucker-Prager elastoplasticity flow rule, enhanced by a novel particle density estimation method for converting particles between representations of either continuum or discrete points. Fluid and sediment are two-way coupled through a momentum exchange force that can be easily resolved with two MPM background grids. We present various results to demonstrate the efficacy of our method.

The Material Point Method (MPM) has been shown to facilitate effective simulations of physically complex and topologically challenging materials, with a wealth of emerging applications in computational engineering and visual computing. Borne out of the extreme importance of regularity, MPM is given attractive parallelization opportunities on high-performance modern multiprocessors. Parallelization of MPM that fully leverages computing resources presents challenges that require exploring an extensive design-space for favorable data structures and algorithms. Unlike the conceptually simple CPU parallelization, where the coarse partition of tasks can be easily applied, it takes greater effort to reach the GPU hardware saturation due to its many-core SIMT architecture. In this paper we introduce methods for addressing the computational challenges of MPM and extending the capabilities of general simulation systems based on MPM, particularly concentrating on GPU optimization. In addition to our open-source high-performance framework, we also conduct performance analyses and benchmark experiments to compare against alternative design choices which may superficially appear to be reasonable, but can suffer from suboptimal performance in practice. Our explicit and fully implicit GPU MPM solvers are further equipped with a Moving Least Squares MPM heat solver and a novel sand constitutive model to enable fast simulations of a wide range of materials. We demonstrate that more than an order of magnitude performance improvement can be achieved with our GPU solvers. Practical high-resolution examples with up to ten million particles run in less than one minute per frame.

We present an efficient and scalable octree-inspired fluid simulation framework with the flexibility to leverage adaptivity in any part of the computational domain, even when resolution transitions reach the free surface. Our methodology ensures symmetry, definiteness and second order accuracy of the discrete Poisson operator, and eliminates numerical and visual artifacts of prior octree schemes. This is achieved by adapting the operators acting on the octree's simulation variables to reflect the structure and connectivity of a power diagram , which recovers primal-dual mesh orthogonality and eliminates problematic T-junction configurations. We show how such operators can be efficiently implemented using a pyramid of sparsely populated uniform grids, enhancing the regularity of operations and facilitating parallelization. A novel scheme is proposed for encoding the topology of the power diagram in the neighborhood of each octree cell, allowing us to locally reconstruct it on the fly via a lookup table, rather than resorting to costly explicit meshing. The pressure Poisson equation is solved via a highly efficient, matrix-free multigrid preconditioner for Conjugate Gradient, adapted to the power diagram discretization. We use another sparsely populated uniform grid for high resolution interface tracking with a narrow band level set representation. Using the recently introduced SPGrid data structure, sparse uniform grids in both the power diagram discretization and our narrow band level set can be compactly stored and efficiently updated via streaming operations. Additionally, we present enhancements to adaptive level set advection, velocity extrapolation, and the fast marching method for redistancing. Our overall framework gracefully accommodates the task of dynamically adapting the octree topology during simulation. We demonstrate end-to-end simulations of complex adaptive flows in irregularly shaped domains, with tens of millions of degrees of freedom.

We present an adaptive Generalized Interpolation Material Point (GIMP) method for simulating elastoplastic materials. Our approach allows adaptive refining and coarsening of different regions of the material, leading to an efficient MPM solver that concentrates most of the computation resources in specific regions of interest. We propose a C 1 continuous adaptive basis function that satisfies the partition of unity property and remains non-negative throughout the computational domain. We develop a practical strategy for particle-grid transfers that leverages the recently introduced SPGrid data structure for storing sparse multi-layered grids. We demonstrate the robustness and efficiency of our method on the simulation of various elastic and plastic materials. We also compare key kernel components to uniform grid MPM solvers to highlight performance benefits of our method.

Abstract We present a shape manipulation technique capable of producing deformations of 2D and 3D meshes, guaranteeing that no elements will be inverted. We achieve this by augmenting the quadratic ex‐rotated elastic energy with additional convex terms that penalize the presence of inverted elements. Using a schedule of increasing penalty coefficients, we efficiently and robustly converge to an inversion free state by solving a sequence of unconstrained convex minimization problems. This process can be interpreted as a special purpose Semi‐Definite Programming (SDP) solver. We demonstrate that our method outperforms solvers used in previous work, including commercial‐grade SDP software (MOSEK). As an additional benefit, our method also converges to the solution via a more intuitive path, which can be used for quick preview. We demonstrate the efficacy of our scheme in a number of 2D and 3D shapes undergoing moderate to drastic deformation.