Table of Contents

• Surrogate Modeling for Physical Systems with Preserved Properties and Adjustable TradeoffsRandi Wang, Morad Behandish. [doi]
• MatVAE: Independently Trained Nested Variational Autoencoder for Generating Chemical Structural FormulaYoshihiro Osakabe, Akinori Asahara. [doi]
• Bayesian-Inference-based Inverse Estimation of Small Angle ScatteringAkinori Asahara, Hidekazu Morita, Kanta Ono, Masao Yano, Chiharu Mitsumata, Tetsuya Shoji, Kotaro Saito. [doi]
• Partition of Unity Networks: Deep HP-ApproximationKookjin Lee, Nathaniel Trask, Ravi G. Patel, Mamikon A. Gulian, Eric C. Cyr. [doi]
• Reduced-order Model for Fluid Flows via Neural Ordinary Differential EquationsCarlos Jose Gonzalez Rojas, Andreas Dengel 0001, Mateus Dias Ribeiro. [doi]
• Learning Dynamical Systems across EnvironmentsYuan Yin, Ibrahim Ayed, Emmanuel de Bézenac, Patrick Gallinari. [doi]
• Discovery of Physics and Characterization of Microstructure from Data with Bayesian Hidden Physics ModelsSteven Atkinson, Yiming Zhang, Liping Wang. [doi]
• Applications of Koopman Mode Analysis to Neural NetworksRyan Mohr, Maria Fonoberova, Iva Manojlovic, Aleksandr Andrejcuk, Zlatko Drmac, Yannis Kevrekidis, Igor Mezic. [doi]
• Learning Physics-guided Neural Networks with Competing Physics Loss: A Summary of Results in Solving Eigenvalue ProblemsMohannad Elhamod, Jie Bu, Christopher Singh, Matthew Redell, Abantika Ghosh, Viktor Podolskiy, Wei-Cheng Lee, Anuj Karpatne. [doi]
• Accelerating Simulation of Stiff Nonlinear Systems using Continuous-Time Echo State NetworksRanjan Anantharaman, Yingbo Ma, Shashi Gowda, Chris Laughman, Viral B. Shah, Alan Edelman, Christopher Rackauckas. [doi]
• Graph-Informed Neural NetworksSøren Taverniers, Eric Joseph Hall, Markos A. Katsoulakis, Daniel M. Tartakovsky. [doi]
• Learning the Principle of Least Action with Reinforcement LearningZehao Jin, Joshua Yao-Yu Lin, Siao-Fong Li. [doi]
• Modeling Physically-Consistent, Chaotic Spatiotemporal Dynamics with Echo State NetworksAlisha Sharma, Kaiyan Shi, Yiling Qiao, Matthew Ziemann. [doi]
• Self-Adaptive Physics-Informed Neural Networks using a Soft Attention MechanismLevi D. McClenny, Ulisses M. Braga-Neto. [doi]
• Machine Learning Application for Permeability Estimation of Three-Dimensional Rock ImagesHongkyu Yoon, Darryl J. Melander, Stephen J. Verzi. [doi]
• A Deep Learning Algorithm for Piecewise Linear Interface Construction (PLIC)Mohammadmehdi Ataei, Erfan Pirmorad, Franco Costa, Sejin Han, Chul B. Park, Markus Bussmann. [doi]
• Neural Ordinary Differential Equations for Data-Driven Reduced Order Modeling of Environmental HydrodynamicsSourav Dutta, Peter Rivera-Casillas, Matthew W. Farthing. [doi]
• Physics-informed Neural Networks for Solving Coupled Flow and Transport SystemSanghyun Lee, Teeratorn Kadeethum. [doi]
• Deep Autoencoders for Nonlinear Physics-Constrained Data-Driven Computational Framework with Application to Biological Tissue ModelingXiaolong He, Qizhi He, Jiun-Shyan Chen. [doi]
• AI Research Associate for Early-Stage Scientific DiscoveryMorad Behandish, John Maxwell, Johan de Kleer. [doi]
• Sparsely Constrained Neural Networks for Model Discovery of PDEsGert-Jan Both, Gijs Vermarien, Remy Kusters. [doi]
• Accelerating High-fidelity Combustion Simulations with Classification AlgorithmsWai Tong Chung, Aashwin Ananda Mishra, Nikolaos Perakis, Matthias Ihme. [doi]
• Neural Process for Black-box Model Optimization Under Bayesian FrameworkZhongkai Shangguan, Lei Lin, Wencheng Wu, Beilei Xu. [doi]
• Extended Physics-informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition based Deep Learning Framework for Nonlinear Partial Differential EquationsAmeya D. Jagtap, George E. Karniadakis. [doi]
• PrefaceJonghyun Lee, Eric F. Darve, Peter K. Kitanidis, Matthew W. Farthing, Tyler Hesser. [doi]
• Combining Programmable Potentials and Neural Networks for Materials ProblemsRyan Mohr, Allan M. Avila, Soham Ghosh, Ananta Bhattarai, Muqiao Yang, Xintian Feng, Martin Head-Gordon, Ruslan Salakhutdinov, Maria Fonoberova, Igor Mezic. [doi]
• Physics Informed Deep Learning for Well Test AnalysisBalakrishna D. R, Kamalkumar Rathinasamy, Avijit Das, Keerthi Ashwin, Vani Sivasankaran, Soundararajan Rajendran. [doi]
• Data-driven Learning of Nonlocal Models: from high-fidelity simulations to constitutive lawsHuaiqian You, Yue Yu, Stewart Silling, Marta D'Elia. [doi]
• Generalized Physics-Informed Machine Learning for Transient Physical SystemsRishith Ellath Meethal, Leela Sai Prabhat Reddy Kondamadugula, Mohamed Khalil, Birgit Obst, Roland Wüchner. [doi]
• Deep Learning-based Fast Solver of the Shallow Water EquationsMojtaba Forghani, Yizhou Qian, Jonghyun Lee, Matthew W. Farthing, Tyler Hesser, Peter K. Kitanidis, Eric Darve. [doi]
• Physics-Informed Machine Learning Simulator for Wildfire PropagationLuca Bottero, Francesco Calisto, Giovanni Graziano, Valerio Pagliarino, Martina Scauda, Sara Tiengo, Simone Azeglio. [doi]
• ADCME MPI: Distributed Machine Learning for Computational EngineeringKailai Xu, Eric Darve. [doi]
• A Block Coordinate Descent Optimizer for Classification Problems Exploiting ConvexityRavi G. Patel, Nathaniel Trask, Mamikon A. Gulian, Eric C. Cyr. [doi]
• TextureVAE: Learning Interpretable Representations of Material Microstructures Using Variational AutoencodersAvadhut Sardeshmukh, Sreedhar Reddy, Gautham B. P., Pushpak Bhattacharyya. [doi]
• Data Driven Physics Constrained Perturbations for Turbulence Model Uncertainty EstimationJan Felix Heyse, Aashwin Ananda Mishra, Gianluca Iaccarino. [doi]
• Learning Potentials of Quantum Systems using Deep Neural NetworksArijit Sehanobish, Hector H. Corzo, Onur Kara, David van Dijk. [doi]
• Data-based Discovery of Governing EquationsWaad Subber, Piyush Pandita, Sayan Ghosh, Genghis Khan, Liping Wang, Roger G. Ghanem. [doi]
• Graph Networks with Physics-aware Knowledge Informed in Latent SpaceSungyong Seo, Yan Liu. [doi]
• Accurate Machine Learning-based Diagnostic with Quantified UncertaintiesAdi Hanuka, Owen Convery. [doi]
• Greedy Fiedler Spectral Partitioning for Data-driven Discrete Exterior CalculusAndy Huang, Nathaniel Trask, Christopher Brissette, Xiaozhe Hu. [doi]
• LSTMs for Inferring Planetary Boundary Layer Height (PBLH)Zeenat Ali, Dorsa Ziaei, Jennifer Sleeman, Zhifeng Yang, Milton Halem. [doi]
• Stiff-PINN: Physics-Informed Neural Network for Stiff Chemical KineticsWeiqi Ji, Weilun Qiu, Zhiyu Shi, Shaowu Pan, Sili Deng. [doi]
• Model Reduction for the Material Point Method on Nonlinear Manifolds Using Deep LearningPeter Yichen Chen, Maurizio Chiaramonte, Eitan Grinspun, Kevin Carlberg. [doi]
• Learning High-Dimensional Hilbert-Valued Functions With Deep Neural Networks From Limited DataBen Adcock, Simone Brugiapaglia, Nick Dexter, Sebastian Moraga. [doi]
• Toward Geometrical Robustness with Hybrid Deep Learning and Differential Invariants TheoryPierre-Yves Lagrave, Mathieu Riou. [doi]
• Variational Autoencoders for Learning Nonlinear Dynamics of PDEs and ReductionsRyan Lopez, Paul J. Atzberger. [doi]
• Validation of Deep Convolutional Generative Adversarial Networks for High Energy Physics Calorimeter SimulationsFlorian Rehm, Sofia Vallecorsa, Kerstin Borras, Dirk Krücker. [doi]
• Convolutional LSTM for Planetary Boundary Layer Height (PBLH) PredictionDorsa Ziaei, Jennifer Sleeman, Milton Halem, Vanessa Caicedo, Ruben Delgado, Belay Demoz. [doi]