MLPS - Combining AI and ML with Physics Sciences

Use for Physics-informed Learning conferences and seminars.

Maintained by Harry Lee (https://www2.hawaii.edu/~jonghyun/)

Discovery of Physics and Characterization of Microstructure with Bayesian Hidden Physics Models
Discovery of Physics and Characterization of Microstructure with Bayesian Hidden Physics Models
Accelerating high-fidelity combustion simulations with classification algorithms by Wai Tong Chung
Accelerating high-fidelity combustion simulations with classification algorithms by Wai Tong Chung
Physics-Informed Machine Learning Simulator for Wildfire Propagation by Francesco Calisto
Physics-Informed Machine Learning Simulator for Wildfire Propagation by Francesco Calisto
Neural operator: A new paradigm for learning PDEs by Animashree Anandkumar
Neural operator: A new paradigm for learning PDEs by Animashree Anandkumar
Deep learning for scientific computing: (closing) the gap between theory and practice by Ben Adcock
Deep learning for scientific computing: (closing) the gap between theory and practice by Ben Adcock
Targeted use of deep learning for physics-informed model discovery by Nathan Kutz
Targeted use of deep learning for physics-informed model discovery by Nathan Kutz
Weaving together machine learning, theoretical physics, and neuroscience by Surya Ganguli
Weaving together machine learning, theoretical physics, and neuroscience by Surya Ganguli
Graph-Informed Neural Networks by Søren Taverniers
Graph-Informed Neural Networks by Søren Taverniers
Partition of unity networks: deep hp-approximation by Nathaniel Trask
Partition of unity networks: deep hp-approximation by Nathaniel Trask
Accelerating Simulation of Stiff Nonlinear Systems using Continuous-Time Echo State Nets, Rackauckas
Accelerating Simulation of Stiff Nonlinear Systems using Continuous-Time Echo State Nets, Rackauckas
Learning High-Dimensional Hilbert-Valued Functions With DNNs From Limited Data by Nick Dexter
Learning High-Dimensional Hilbert-Valued Functions With DNNs From Limited Data by Nick Dexter
Neural ODEs for Data-Driven Reduced Order Modeling of Environmental Hydrodynamics by Sourav Dutta
Neural ODEs for Data-Driven Reduced Order Modeling of Environmental Hydrodynamics by Sourav Dutta
Data-driven learning of nonlocal models: from high-fidelity simulations to constitutive laws, D'Elia
Data-driven learning of nonlocal models: from high-fidelity simulations to constitutive laws, D'Elia
Timesmash: Process-aware Fast Time Series Clustering/Classification w/ Hidden Stochastic Generators
Timesmash: Process-aware Fast Time Series Clustering/Classification w/ Hidden Stochastic Generators
Model Reduction for the Material Point Method on Nonlinear Manifolds Using DL by Peter Yichen Chen
Model Reduction for the Material Point Method on Nonlinear Manifolds Using DL by Peter Yichen Chen
Learning Physics-guided Neural Networks with Competing Physics Loss: Solving Eigenvalue Problems
Learning Physics-guided Neural Networks with Competing Physics Loss: Solving Eigenvalue Problems
Greedy Fiedler Spectral Partitioning for Data-driven Discrete Exterior Calculus by Andy Huang
Greedy Fiedler Spectral Partitioning for Data-driven Discrete Exterior Calculus by Andy Huang
ADCME MPI: Distributed Machine Learning for Computational Engineering by Kailai Xu
ADCME MPI: Distributed Machine Learning for Computational Engineering by Kailai Xu
A block coordinate descent optimizer for classification problems exploiting convexity by Gulian
A block coordinate descent optimizer for classification problems exploiting convexity by Gulian
Toward Geometrical Robustness with Hybrid DL and Differential Invariants Theory by Lagrave
Toward Geometrical Robustness with Hybrid DL and Differential Invariants Theory by Lagrave
Variational Autoencoders for Learning Nonlinear Dynamics of Physical Systems by Ryan Lopez
Variational Autoencoders for Learning Nonlinear Dynamics of Physical Systems by Ryan Lopez
Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism by Levi McClenny
Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism by Levi McClenny
Combining Programmable Potentials and Neural Networks for Materials Problems by Ryan Mohr
Combining Programmable Potentials and Neural Networks for Materials Problems by Ryan Mohr
Neural Process for Black-box Model Optimization Under Bayesian Framework by Zhongkai Shangguan
Neural Process for Black-box Model Optimization Under Bayesian Framework by Zhongkai Shangguan
Generalized Physics-Informed Machine Learning for Transient Physical Systems by Rishith E. Meethal
Generalized Physics-Informed Machine Learning for Transient Physical Systems by Rishith E. Meethal
Stiff-PINN: Physics-Informed Neural Network for Stiff Chemical Kinetics by Weiqi Ji
Stiff-PINN: Physics-Informed Neural Network for Stiff Chemical Kinetics by Weiqi Ji
Data Driven & Physics Constrained Perturbations for Turbulence Model Uncertainty Estimation by Heyse
Data Driven & Physics Constrained Perturbations for Turbulence Model Uncertainty Estimation by Heyse
AE for Nonlinear Physics-Constrained Data-Driven Computational Framework: Biological Tissue Modeling
AE for Nonlinear Physics-Constrained Data-Driven Computational Framework: Biological Tissue Modeling
Graph Networks with Physics-aware Knowledge Informed in Latent Space by Sungyong Seo
Graph Networks with Physics-aware Knowledge Informed in Latent Space by Sungyong Seo
ML Application for Permeability Estimation of Three-Dimensional Rock Images by Hongkyu Yoon
ML Application for Permeability Estimation of Three-Dimensional Rock Images by Hongkyu Yoon