Geological CO2 storage (GCS) involves injecting captured CO2 into deep subsurface formations to support climate goals. The effective management of GCS relies on adaptive injection planning to dynamically control injection rates and well pressures to balance both storage safety and efficiency. Prior literature, including numerical optimization methods and surrogate-optimization methods, is limited by real-world GCS requirements of smooth state transitions and goal-directed planning within limited time. To address these limitations, we propose a Brownian Bridge–augmented framework for surrogate simulation and injection planning in GCS and develop two insights (i) Brownian bridge as smooth state regularizer for better surrogate simulator; (ii) Brownian bridge as goal-time-conditioned planning guidance for better injection planning. Our method has three stages: (i) learning deep Brownian bridge representations with contrastive and reconstructive losses from historical reservoir and utility trajectories, (ii) incorporating Brownian bridge-based next state interpolation for simulator regularization (iii) guiding injection planning with Brownian utility-conditioned trajectories to generate high-quality injection plans. Experimental results across multiple datasets collected from diverse GCS settings demonstrate that our framework consistently improves simulation fidelity and planning effectiveness while maintaining low computational overhead.

Bayesian Optimization (BO) is a widely used technique for optimizing black-box functions, whose mathematical form is unknown and can only be evaluated through costly simulations. In this work, we focus on optimizing functions that are the \(\min/\max\) of several components, where each component is typically a non-linear, non-convex black-box function. Traditional BO approaches may suffer from the masking effect , where components with lower mean values are sampled more, even if they do not contain the global optimum. In our prior work, Minimum Bayesian Optimization ( minBO ) addressed this issue by proposing a sampling approach that uses a surrogate for each component and samples based on predicted improvement across all components. While effective, minBO treats each component independently and does not capture potential dependencies between components. Additionally, estimating surrogates for each component can limit the scalability of the approach. We introduce Conjunctive Bayesian Optimization ( conBO ), a novel approach that overcomes these limitations. We propose a paired sampling algorithm ( conBO-PS ) that considers dependencies between components by analyzing all pairs of functions and estimating the distribution of the minimum of two functions. While conBO-PS accounts for dependencies, it is computationally expensive. To improve efficiency, we introduce conBO large-scale ( conBO-LS ), which adapts conBO-PS by considering a subset of components chosen based on their potential impact, allowing users to control computational effort. We evaluate the performance of these algorithms on non-linear synthetic functions and also compare them to state-of-the-art methods in the context of falsifying conjunctive safety requirements for Cyber-Physical Systems (CPS). A conjunctive safety requirement refers to a set of safety conditions (requirements) tested together, where if at-least one condition is falsified, the entire conjunctive requirement is considered falsified. In fact, in such context the function to be minimized is the minimum of several sub-components. Results show that conBO-PS and conBO-LS outperform existing approaches, offering better solution quality and computational efficiency. In the CPS application, the proposed approaches achieve faster falsification and improved falsification rates across all benchmarks.

BACKGROUND: The active functionalities of RNA are recognized to be heavily dependent on the structure and sequence. Therefore, a model that can accurately evaluate a design by giving RNA sequence-structure pairs would be a valuable tool for many researchers. Machine learning methods have been explored to develop such tools, showing promising results. However, two key issues remain. Firstly, the performance of machine learning models is affected by the features used to characterize RNA. Currently, there is no consensus on which features are the most effective for characterizing RNA sequence-structure pairs. Secondly, most existing machine learning methods extract features describing entire RNA molecule. We argue that it is essential to define additional features that characterize nucleotides and specific sections of RNA structure to enhance the overall efficacy of the RNA design process. RESULTS: We develop two deep learning models for evaluating RNA sequence-secondary structure pairs. The first model, NU-ResNet, uses a convolutional neural network architecture that solves the aforementioned problems by explicitly encoding RNA sequence-structure information into a 3D matrix. Building upon NU-ResNet, our second model, NUMO-ResNet, incorporates additional information derived from the characterizations of RNA, specifically the 2D folding motifs. In this work, we introduce an automated method to extract these motifs based on fundamental secondary structure descriptions. We evaluate the performance of both models on an independent testing dataset. Our proposed models outperform the models from literatures in this independent testing dataset. To assess the robustness of our models, we conduct 10-fold cross validation. To evaluate the generalization ability of NU-ResNet and NUMO-ResNet across different RNA families, we train and test our proposed models in different RNA families. Our proposed models show superior performance compared to the models from literatures when being tested across different independent RNA families. CONCLUSIONS: In this study, we propose two deep learning models, NU-ResNet and NUMO-ResNet, to evaluate RNA sequence-secondary structure pairs. These two models expand the field of data-driven approaches for learning RNA. Furthermore, these two models provide the new method to encode RNA sequence-secondary structure pairs.

Requirements driven search-based testing (also known as falsification) has proven to be a practical and effective method for discovering erroneous behaviors in Cyber-Physical Systems. Despite the constant improvements on the performance and applicability of falsification methods, they all share a common characteristic. Namely, they are best-effort methods which do not provide any guarantees on the absence of erroneous behaviors (falsifiers) when the testing budget is exhausted. The absence of finite time guarantees is a major limitation which prevents falsification methods from being utilized in certification procedures. In this paper, we address the finite-time guarantees problem by developing a new stochastic algorithm. Our proposed algorithm not only estimates (bounds) the probability that falsifying behaviors exist, but also identifies the regions where these falsifying behaviors may occur. We demonstrate the applicability of our approach on standard benchmark functions from the optimization literature and on the F16 benchmark problem.Note to Practitioners—The safety assurance problem for Cyber-Physical Systems (CPS) remains an open challenge. To demonstrate functional safety, practitioners must collect evidence that establishes that a system performs as expected under certain assumptions. The expected system behavior is typically captured through functional correctness requirements. In the case of CPS, evidence typically takes the form of test cases that are executed both on a model of the system and/or on the actual system. One of the challenges in producing such evidence is how to automatically generate test cases which are representative of the infinite execution space of CPS. Search-based test generation (SBTG) is a class of methods that can automatically generate test cases for CPS while being guided by the functional requirements. As SBTG methods try to discover test cases that invalidate, i.e., falsify, the requirements, they also collect validating, i.e., satisfying, test cases that can be used as evidence. This work introduces a method that can assess whether enough test cases have been executed given a finite testing budget. The sufficiency of the test suite is assessed by computing the probability that invalidating system behaviors may exist but have not yet been discovered. The practitioner can then adjust the number of test cases generated until a desired degree of confidence on the probability is achieved. Hence, our method not only works as an automated test case generation algorithm, but also as a method that provides formal functional performance guarantees on the system. Future directions will investigate extensions of our method to stochastic CPS.

We propose a framework for security vulnerability analysis for Cyber-Physical Systems (CPS). Our framework imposes only minimal assumptions on the structure of the CPS. Namely, we consider CPS with feedback control loops, state observers, and anomaly detection algorithms. Moreover, our framework does not require any knowledge about the dynamics or the algorithms used in the CPS. Under this common CPS architecture, we develop tools that can identify vulnerabilities in the system and their impact on the functionality of the CPS. We pose the CPS security problem as a falsification (or Search Based Test Generation (SBTG)) problem guided by security requirements expressed in Signal Temporal Logic (STL). We propose two different categories of security requirements encoded in STL: (1) detectability (stealthiness) and (2) effectiveness (impact on the CPS function). Finally, we demonstrate in simulation on an inverted pendulum and on an Unmanned Aerial Vehicle (UAV) that both specifications are falsifiable using our SBTG techniques.

In the paper titled " Stealthy attacks formalized as STL formulas for Falsification of CPS Security", we investigate a broad class of attacks on the sensor and actuation blocks in the form of additive perturbation that impacts the measurement and control, respectively. In this demo, we demonstrate the usage of our framework and the underlying technologies along with a case study on aviation systems using Microsoft Flight Simulator (MSFS).

Ribonucleic acid (RNA) is a fundamental biological molecule that is essential to all living organisms, performing a versatile array of cellular tasks. The function of many RNA molecules is strongly related to the structure it adopts. As a result, great effort is being dedicated to the design of efficient algorithms that solve the “folding problem”—given a sequence of nucleotides, return a probable list of base pairs, referred to as the secondary structure prediction. Early algorithms largely rely on finding the structure with minimum free energy. However, the predictions rely on effective simplified free energy models that may not correctly identify the correct structure as the one with the lowest free energy. In light of this, new, data-driven approaches that not only consider free energy, but also use machine learning techniques to learn motifs are also investigated and recently been shown to outperform free energy–based algorithms on several experimental data sets. In this work, we introduce the new ExpertRNA algorithm that provides a modular framework that can easily incorporate an arbitrary number of rewards (free energy or nonparametric/data driven) and secondary structure prediction algorithms. We argue that this capability of ExpertRNA has the potential to balance out different strengths and weaknesses of state-of-the-art folding tools. We test ExpertRNA on several RNA sequence-structure data sets, and we compare the performance of ExpertRNA against a state-of-the-art folding algorithm. We find that ExpertRNA produces, on average, more accurate predictions of nonpseudoknotted secondary structures than the structure prediction algorithm used, thus validating the promise of the approach. Summary of Contribution: ExpertRNA is a new algorithm inspired by a biological problem. It is applied to solve the problem of secondary structure prediction for RNA molecules given an input sequence. The computational contribution is given by the design of a multibranch, multiexpert rollout algorithm that enables the use of several state-of-the-art approaches as base heuristics and allowing several experts to evaluate partial candidate solutions generated, thus avoiding assuming the reward being optimized by an RNA molecule when folding. Our implementation allows for the effective use of parallel computational resources as well as to control the size of the rollout tree as the algorithm progresses. The problem of RNA secondary structure prediction is of primary importance within the biology field because the molecule structure is strongly related to its functionality. Whereas the contribution of the paper is in the algorithm, the importance of the application makes ExpertRNA a showcase of the relevance of computationally efficient algorithms in supporting scientific discovery.

Requirements-driven search-based testing (SBT), also known as falsification, has proven to be a practical and effective method for discovering erroneous behaviors in Cyber-Physical Systems. However, SBT techniques do not provide guarantees on correctness if no falsifying behavior is found within the test budget. Hence, the applicability of SBT methods for evidence generation supporting assurance cases is limited. In this work, we make progress towards developing finite-time guarantees for SBT techniques with associated confidence metrics. We demonstrate the applicability of our approach to the F16 GCAS benchmark challenge.

Safety is a primary concern for the various transport means. For sea transport, this includes various aspects like human safety at sea and at port, and also environmental safety and sustainability. In heavy-traffic regions where the waters are congested and vessels sail very closely together, ensuring these safety needs can be challenging. In this paper, we leverage on the rich information transmitted through the automatic identification system (AIS) and propose, for the first time, an integrated simulation-optimization approach for real time collision avoidance. This enables capturing of stochastic dynamic behavior of vessels for better prediction and fast trajectory optimization for application in real time. Specifically, a realistic agent-based model is developed based on behavioral learning in a real-environment, and incorporated into a fast collision avoidance optimization model in real time to provide robust collision avoidance that is able to account for future stochastic consequences of the actions taken. To achieve this, we develop: 1) a vessel pattern recognition method that mines the rich AIS data to produce realistic trajectory models; 2) an agent-based simulation model to enhance future trajectory prediction; and 3) a fast surrogate-based sampling technique to generate collision avoidance maneuvers for vessel captains in real time. To illustrate the feasibility of the approach, we use the case of the Singapore strait, one of the busiest straits in the world.

The N-k problem is very well known in the power industry and it tries to answer the question whether there exists a set of k lines in a power network with N elements whose removal would cause the failure of the system. In practice, it is common to evaluate a system according to an N-1 criterion, i.e., k = 1. While this problem has traditionally been considered in a deterministic setting, stochastic behavior within the system is important especially in the context of extreme events. A number of stochastic Monte Carlo models have been proposed to estimate the probability of cascading failures. In this paper, we deal with simulation budget allocation of the stochastic N-1 problem. More specifically, we assume that a simulation model is able to provide us an estimate of the system failure rate when any line is tripped. It is not difficult to see how simulation of all configurations to some certain accuracy can become computationally expensive with the growth of N. Under such a setting, we transform the N-1 problem into a stochastic selection process with optimal computing budget allocation (OCBA): given N configurations, we would like to sequentially allocate a certain number of simulation replications in order to answer the question whether the system is reliable or not. We show through theoretical analysis and numerical experiments that the probability of correctly identifying the system reliability state can be increased by applying OCBA allocation rules in the simulation budget allocation process.