Discovering evolutionary traits that are heritable across species on the tree of life (also referred to as a phylogenetic tree) is of great interest to biologists to understand how organisms diversify and evolve. However, the measurement of traits is often a subjective and labor-intensive process, making trait discovery a highly label-scarce problem. We present a novel approach for discovering evolutionary traits directly from images without relying on trait labels. Our proposed approach, Phylo-NN, encodes the image of an organism into a sequence of quantized feature vectors -or codes- where different segments of the sequence capture evolutionary signals at varying ancestry levels in the phylogeny. We demonstrate the effectiveness of our approach in producing biologically meaningful results in a number of downstream tasks including species image generation and species-to-species image translation, using fish species as a target example

Inferring the source information of greenhouse gases, such as methane, from spatially sparse sensor observations is an essential element in mitigating climate change. While it is well understood that the complex behavior of the atmospheric dispersion of such pollutants is governed by the Advection-Diffusion equation, it is difficult to directly apply the governing equations to identify the source location and magnitude (inverse problem) because of the spatially sparse and noisy observations, i.e., the pollution concentration is known only at the sensor locations and sensors sensitivity is limited. Here, we develop a multi-task learning framework that can provide high-fidelity reconstruction of the concentration field and identify emission characteristics of the pollution sources such as their location, emission strength, etc. from sparse sensor observations. We demonstrate that our proposed framework is able to achieve accurate reconstruction of the methane concentrations from sparse sensor measurements as well as precisely pin-point the location and emission strength of these pollution sources.

Physics-guided Neural Networks (PGNNs) represent an emerging class of neural networks that are trained using physics-guided (PG) loss functions (capturing violations in network outputs with known physics), along with the supervision contained in data. Existing work in PGNNs has demonstrated the efficacy of adding single PG loss functions in the neural network objectives, using constant tradeoff parameters, to ensure better generalizability. However, in the presence of multiple PG functions with competing gradient directions, there is a need to adaptively tune the contribution of different PG loss functions during the course of training to arrive at generalizable solutions. We demonstrate the presence of competing PG losses in the generic neural network problem of solving for the lowest (or highest) eigenvector of a physics-based eigenvalue equation, which is commonly encountered in many scientific problems. We present a novel approach to handle competing PG losses and demonstrate its efficacy in learning generalizable solutions in two motivating applications of quantum mechanics and electromagnetic propagation. All the code and data used in this work are available at https://github.com/jayroxis/Cophy-PGNN.

Generating flow fields (such as pressure and velocity fields) in 3D space is a fundamental task in computational fluid dynamics (CFD), with applications across a vast spectrum of science and engineering problems. An important class of fluid flow problems in CFD is multi-phase flow, where dispersed solid particles are present in the fluid flow. Despite recent developments in deep learning (DL) for CFD applications, current state-of-the-art is still unable to model 3D flow fields, especially in multi-phase flow settings. It is with this goal that we introduce PhyFlow, a novel physics-guided deep learning architecture for modeling 3D multi-phase fluid flows, designed to mimic the popular projection method for solving fluid flows in CFD simulations. We demonstrate that PhyFlow generates high quality flow fields and yields a 49.61% improvement over other state-of-the-art baselines. We also test the quality of PhyFlow based fields by employing them in downstream tasks like particle drag force prediction and demonstrate state-of-the-art results, improving upon the previous best models by 9.89%. Finally, we demonstrate the consistency of PhyFlow predictions with known underlying physics governing equations. Our source code and data are available online*.*tinyurl.com/mjkcrsdw

As applications of deep learning (DL) continue to seep into critical scientific use-cases, the importance of performing uncertainty quantification (UQ) with DL has become more pressing than ever before. In scientific applications, it is also important to inform the learning of DL models with knowledge of physics of the problem to produce physically consistent and generalized solutions. This is referred to as the emerging field of physics-informed deep learning (PIDL). We consider the problem of developing PIDL formulations that can also perform UQ. To this end, we propose a novel physics-informed GAN architecture, termed PID-GAN, where the knowledge of physics is used to inform the learning of both the generator and discriminator models, making ample use of unlabeled data instances. We show that our proposed PID-GAN framework does not suffer from imbalance of generator gradients from multiple loss terms as compared to state-of-the-art. We also empirically demonstrate the efficacy of our proposed framework on a variety of case studies involving benchmark physics-based PDEs as well as imperfect physics. All the code and datasets used in this study have been made available on this link: https://github.com/arkadaw9/PID-GAN.

There is a great interest in scientific communities for harnessing the power of AI in applications ranging from climate science to quantum chemistry. The common theme in many of these applications is that the data are spatiotemporal with governing physics. Unfortunately, today's ML approaches are mostly purely data-driven, i.e., they solely rely on (labeled) data for learning statistical patterns. Collecting labeled data can be quite expensive in real-world applications. Moreover, the resulting black-box AI models are difficult to interpret for domain scientists.

We propose quadratic residual networks (QRes) as a new type of parameter-efficient neural network architecture, by adding a quadratic residual term to the weighted sum of inputs before applying activation functions. With sufficiently high functional capacity (or expressive power), we show that it is especially powerful for solving forward and inverse physics problems involving partial differential equations (PDEs). Using tools from algebraic geometry, we theoretically demonstrate that, in contrast to plain neural networks, QRes shows better parameter efficiency in terms of network width and depth thanks to higher non-linearity in every neuron. Finally, we empirically show that QRes shows faster convergence speed in terms of number of training epochs especially in learning complex patterns.

The objective of unsupervised graph representation learning (GRL) is to learn a low-dimensional space of node embeddings that reflect the structure of a given unlabeled graph.Existing algorithms for this task rely on negative sampling objectives that maximize the similarity in node embeddings at nearby nodes (referred to as "cohesion") by maintaining positive and negative corpus of node pairs.While positive samples are drawn from node pairs that co-occur in short random walks, conventional approaches construct negative corpus by uniformly sampling random pairs, thus ignoring valuable information about structural dissimilarity among distant node pairs (referred to as "separation").In this paper, we present a novel Distance-aware Negative Sampling (DNS) which maximizes the separation of distant node-pairs while maximizing cohesion at nearby node-pairs by setting the negative sampling probability proportional to the pair-wise shortest distances.Our approach can be used in conjunction with any GRL algorithm and we demonstrate the efficacy of our approach over baseline negative sampling methods over downstream node classification tasks on a number of benchmark datasets and GRL algorithms.All our codes and datasets are available at https://github.com/ Distance-awareNS/DNS/.

To simultaneously address the rising need of expressing uncertainties in deep learning models along with producing model outputs which are consistent with the known scientific knowledge, we propose a novel physics-guided architecture (PGA) of neural networks in the context of lake temperature modeling where the physical constraints are hard coded in the neural network architecture. This allows us to integrate such models with state of the art uncertainty estimation approaches such as Monte Carlo (MC) Dropout without sacrificing the physical consistency of our results. We demonstrate the effectiveness of our approach in ensuring better generalizability as well as physical consistency in MC estimates over data collected from Lake Mendota in Wisconsin and Falling Creek Reservoir in Virginia, even with limited training data. We further show that our MC estimates correctly match the distribution of ground-truth observations, thus making the PGA paradigm amenable to physically grounded uncertainty quantification.

Physics-based simulations are often used to model and understand complex physical systems in domains like fluid dynamics. Such simulations although used frequently, often suffer from inaccurate or incomplete representations either due to their high computational costs or due to lack of complete physical knowledge of the system. In such situations, it is useful to employ machine learning to fill the gap by learning a model of the complex physical process directly from simulation data. However, as data generation through simulations is costly, we need to develop models being cognizant of data paucity issues. In such scenarios it is helpful if the rich physical knowledge of the application domain is incorporated in the architectural design of machine learning models. We can also use information from physics-based simulations to guide the learning process using aggregate supervision to favorably constrain the learning process. In this paper, we propose PhyNet, a deep learning model using physics-guided structural priors and physics-guided aggregate supervision for modeling the drag forces acting on each particle in a Computational Fluid Dynamics-Discrete Element Method (CFD-DEM). We conduct extensive experiments in the context of drag force prediction and showcase the usefulness of including physics knowledge in our deep learning formulation. PhyNet has been compared to several state-of-the-art models and achieves a significant performance improvement of 8.46% on average. The source code has been made available∗ and the dataset used is detailed in [1, 2].

Detecting land use and land cover changes is critical to monitor natural resources and analyze global environmental changes. In this paper, we investigate the land cover detection using the remote sensing data from earth-observing satellites. Due to the natural disturbances, e.g., clouds and aerosoles, and the data acquisition errors by devices, remote sensing data frequently contain much noise. Also, many land covers cannot be easily identified in most dates of a year. Instead, they show distinctive temporal patterns only during certain period of a year, which is also referred to as the discriminative period. To address these challenges, we propose a novel framework which combines the spatial context knowledge with the LSTM-based temporal modeling for land cover detection. Specifically, the framework learns the spatial context knowledge selectively from its neighboring locations. Then we propose two approaches for discriminative period detection based on multi-instance learning and local attention mechanism, respectively. Our evaluations in two real-world applications demonstrate the effectiveness of the proposed method in identifying land covers and detecting discriminative periods.

Recent advances in processing remote sensing data have provided unprecedented potential for monitoring land covers. However, it is extremely challenging to deploy an automated monitoring system for different regions and across different years given the involved data heterogeneity over space and over time. The heterogeneity exists on two aspects. First, for many land covers, the distinguishing temporal patterns are only visible in certain discriminative period. Due to the change of weather conditions, the discriminative period can shift across space and time, which causes heterogeneity to the sequential data. Second, the collected remote sensing data are affected by acquisition devices and natural variables, e.g., precipitation and sunlight. In this paper, we introduce a novel framework to effectively detect land covers using the sequential remote sensing data. At the same time, we propose new learning strategies based on attention networks and domain adaptation to addresses the aforementioned challenges. The evaluation on two real-world applications - cropland mapping and burned area detection, demonstrate that the proposed method can effectively detect land covers under different weather conditions.

We propose a nonlinear manifold learning technique based on deep convolutional autoencoders that is appropriate for model order reduction of physical systems in complex geometries. Convolutional neural networks have proven to be highly advantageous for compressing data arising from systems demonstrating a slow-decaying Kolmogorov $n$-width. However, these networks are restricted to data on structured meshes. Unstructured meshes are often required for performing analyses of real systems with complex geometry. Our custom graph convolution operators based on the available differential operators for a given spatial discretization effectively extend the application space of deep convolutional autoencoders to systems with arbitrarily complex geometry that are typically discretized using unstructured meshes. We propose sets of convolution operators based on the spatial derivative operators for the underlying spatial discretization, making the method particularly well suited to data arising from the solution of partial differential equations. We demonstrate the method using examples from heat transfer and fluid mechanics and show better than an order of magnitude improvement in accuracy over linear methods.

Geosciences is a field of great societal relevance that requires solutions to several urgent problems facing our humanity and the planet. As geosciences enters the era of big data, machine learning (ML)-that has been widely successful in commercial domains-offers immense potential to contribute to problems in geosciences. However, geoscience applications introduce novel challenges for ML due to combinations of geoscience properties encountered in every problem, requiring novel research in machine learning. This article introduces researchers in the machine learning (ML) community to these challenges offered by geoscience problems and the opportunities that exist for advancing both machine learning and geosciences. We first highlight typical sources of geoscience data and describe their common properties. We then describe some of the common categories of geoscience problems where machine learning can play a role, discussing the challenges faced by existing ML methods and opportunities for novel ML research. We conclude by discussing some of the cross-cutting research themes in machine learning that are applicable across several geoscience problems, and the importance of a deep collaboration between machine learning and geosciences for synergistic advancements in both disciplines.

In recent years, the large amount of labeled data available has also helped tend research toward using minimal domain knowledge, e.g., in deep neural network research. However, in many situations, data is limited and of poor quality. Can domain knowledge be useful in such a setting? In this paper, we propose domain adapted neural networks (DANN) to explore how domain knowledge can be integrated into model training for deep networks. In particular, we incorporate loss terms for knowledge available as monotonicity constraints and approximation constraints. We evaluate our model on both synthetic data generated using the popular Bohachevsky function and a real-world dataset for predicting oxygen solubility in water. In both situations, we find that our DANN model outperforms its domain-agnostic counterpart yielding an overall mean performance improvement of 19.5% with a worst- and best-case performance improvement of 4% and 42.7%, respectively.

Automated cropland monitoring can offer timely and reliable agricultural information, which is critical to meet the increasing demand for food supply and food security. In most cropland mapping tasks, domain researchers provide manually labeled training samples for several major crop types and request for identifying these major crops in a target region. However, it is very expensive to hire experts to label all the other land covers that exist in the target region. In this paper, we propose a novel learning framework to identify major crops without using labeled training samples for other land covers. For each major crop type, we train a one-class classification model based on sparse-autoencoder (SAE). Specifically, we utilize the high-resolution (~10m) remote sensing data as input features to classify each location either as one of major crop types or as other land covers. Many crop types are similar to each other in most dates of a year, but are distinguishable only during a short period in growing season. To better model the seasonal patterns of different crop types and to capture the their discriminative periods, we introduce a sliding window to cover different growing periods in a year and learn separate SAEs from these periods. Moreover, since remote sensing data are commonly disturbed by natural noise factors, we explore the spatial contiguity of unlabeled data in test region and incorporate it as a constraint in training process to further improve the performance. In this way, we utilize both labeled data and unlabeled data in a semi-supervised method to jointly train SAE. Finally, we design a mechanism to combine the SAEs trained for different crop types to make final classification decisions. We extensively evaluate the proposed method in mapping several major crops in Minnesota, US. The experimental results demonstrate that the proposed method can accurately map the extent of major crops, and capture the temporal growing patterns of different crops. Besides, the results confirm the effectiveness of spatial constraint in mitigating noise factors and making spatially contiguous classification. In addition, we give illustrative examples to show that the proposed method can help detect errors in existing cropland mapping product.

Mining relationships in time series data is of immense interest to several disciplines such as neuroscience, climate science, and transportation. Traditional approaches for mining relationships focus on discovering pair-wise relationships in the data. In this work, we define a novel relationship pattern involving three interacting time series, which we refer to as a tripole. We show that tripoles capture interesting relationship patterns in the data that are not possible to be captured using traditionally studied pair-wise relationships. We demonstrate the utility of tripoles in multiple real-world datasets from various domains including climate science and neuroscience. In particular, our approach is able to discover tripoles that are statistically significant, reproducible across multiple independent data sets, and lead to novel domain insights.

The climate and Earth sciences have recently undergone a rapid transformation from a data-poor to a data-rich environment. In particular, massive amount of data about Earth and its environment is now continuously being generated by a large number of Earth observing satellites as well as physics-based earth system models running on large-scale computational platforms. These massive and information-rich datasets offer huge potential for understanding how the Earth's climate and ecosystem have been changing and how they are being impacted by humans actions. We discuss the challenges involved in analyzing these massive data sets as well as opportunities they present for both advancing machine learning as well as the science of climate change.

Data science models, although successful in a number of commercial domains, have had limited applicability in scientific problems involving complex physical phenomena. Theory-guided data science (TGDS) is an emerging paradigm that aims to leverage the wealth of scientific knowledge for improving the effectiveness of data science models in enabling scientific discovery. The overarching vision of TGDS is to introduce scientific consistency as an essential component for learning generalizable models. Further, by producing scientifically interpretable models, TGDS aims to advance our scientific understanding by discovering novel domain insights. Indeed, the paradigm of TGDS has started to gain prominence in a number of scientific disciplines such as turbulence modeling, material discovery, quantum chemistry, bio-medical science, bio-marker discovery, climate science, and hydrology. In this paper, we formally conceptualize the paradigm of TGDS and present a taxonomy of research themes in TGDS. We describe several approaches for integrating domain knowledge in different research themes using illustrative examples from different disciplines. We also highlight some of the promising avenues of novel research for realizing the full potential of theory-guided data science.

The need for effective change detection is ever growing with more emerging large-scale spatial-temporal datasets that contain gridded time series data. To detect meaningful changing events with respect to our desired characteristics, in this paper we focus on the post-classification change detection problem which aims to apply change detection techniques on the time series of classification outputs. To study the challenges and to evaluate the performance, we apply the change detection techniques to an application of water monitoring using remote sensing data. Since the learning model can be affected by special properties of remote sensing data, the obtained classification outputs usually contain much noise. Therefore the successful change detection requires an elaborate mechanism to handle the time series of noisy classification outputs. To this end we propose to integrate spatial and temporal constraints into an optimization based change detection framework. The proposed framework mitigates the noise in the time series and can be efficiently solved by an EM-style algorithm. The extensive experimental results on both synthetic and real-world datasets very well demonstrate the effectiveness of the proposed method in detecting the water dynamics.

We consider binary classification problems where each of the two classes shows a multi-modal distribution in the feature space, and the classification has to be performed over different test scenarios, where every test scenario only involves a subset of the positive and negative modes in the data. In such conditions, there may exist certain pairs of positive and negative modes, termed as pairs of confusing modes, which may not appear together in the same test scenario but can be highly overlapping in the feature space. Determining the class labels at such pairs of confusing modes is challenging as the labeling decisions depend not only on the feature values but also on the context of the test scenario. To overcome this challenge, we present the Adaptive Heterogeneous Ensemble Learning (AHEL) algorithm, which constructs an ensemble of classifiers in accordance with the multi-modality within the classes, and further assigns adaptive weights to classifiers based on their relevance in the context of a test scenario. We demonstrate the effectiveness of our approach in comparison with baseline approaches on a synthetic dataset and a real-world application involving global water monitoring.

We consider binary classification problems where each of the two classes show multi-modal distribution in the feature space. Inspired by existing ensemble learning methods for multi-class classification, we develop ensemble learning methods for binary classification that make use of the bipartite nature of the positive and negative modes in the data. By constructing ensembles that make use of the multi-modal structure within the two classes, as opposed to using random samples, we are able to ensure sufficient diversity among the classifiers and adequate representation of the modes in the learning of the classifiers. We demonstrate the effectiveness of the proposed ensemble learning methods in comparison with existing approaches over a synthetic dataset and a real-world application involving global lake monitoring, over a broad range of base classifiers.

A large number of real-world domains possess heterogeneity in their data, which implies that different partitions of the data show different relationships between explanatory and response variables. This increases the overall model complexity of predictive learning in the presence of heterogeneity. Additionally, a number of real-world domains lack sufficient training data, making the learning algorithm prone to over-fitting, especially when the model complexity is large. However, there often exists a structure among the data instances and their partitions which can be appropriately leveraged for reducing the model complexity along with addressing heterogeneity. In this paper, we present a framework for learning robust predictive models in real-world heterogeneous datasets which lack sufficient number of training samples. We demonstrate the usefulness of our framework in the domain of remote sensing for forest cover estimation. Through a series of comparative experiments with baseline approaches, we are able to show that our framework: (a) captures meaningful information about heterogeneity in the data, (b) improves prediction performance by addressing data heterogeneity, (c) is robust to over-fitting in the presence of limited training data, and (d) is robust to the choice of the number of partitions used for representing heterogeneity.