Physic Informed

Physics-informed machine learning integrates physical principles and domain knowledge into machine learning models to improve accuracy, efficiency, and interpretability, particularly when data is scarce or complex systems are involved. Current research focuses on developing and applying physics-informed neural networks (PINNs), DeepONets, and other architectures to diverse problems, including solving partial differential equations, modeling physical systems (e.g., fluid dynamics, structural mechanics), and improving AI reasoning. This approach is significantly impacting various fields by enabling faster, more accurate simulations, enhanced model generalization, and the development of more robust and reliable AI systems for scientific discovery and engineering applications.

Papers

April 1, 2022

Data and Physics Driven Learning Models for Fast MRI -- Fundamentals and Methodologies from CNN, GAN to Attention and Transformers
Jiahao Huang, Yingying Fang, Yang Nan, Huanjun Wu, Yinzhe Wu, Zhifan Gao, Yang Li, Zidong Wang, Pietro Lio, Daniel Rueckert, Yonina C. Eldar, Guang Yang
Deep Learning Model Transformer Megatron Decepticons GAN Model CNN Network Physic Informed Fast Magnetic Resonance Imaging Accelerated MRI

March 21, 2022

PI-VAE: Physics-Informed Variational Auto-Encoder for stochastic differential equations
Weiheng Zhong, Hadi Meidani
Generative Adversarial Network Physic Informed Neural Network Variational Autoencoder Stochastic Differential Equation Physic Informed Physic Informed Loss

February 24, 2022

Physics Informed RNN-DCT Networks for Time-Dependent Partial Differential Equations
Benjamin Wu, Oliver Hennigh, Jan Kautz, Sanjay Choudhry, Wonmin Byeon
Physic Informed Neural Network Network Programming Physic Informed Physic Based Time Dependent Partial Differential Equation

February 15, 2022

The efficacy and generalizability of conditional GANs for posterior inference in physics-based inverse problems
Deep Ray, Harisankar Ramaswamy, Dhruv V. Patel, Assad A. Oberai
Generative Adversarial Network Multi Scale Stronger Generalizability Posterior Inference Physic Informed Conditional GANs Physic Based Inverse Problem

February 11, 2022

Bounded nonlinear forecasts of partially observed geophysical systems with physics-constrained deep learning
Said Ouala, Steven L. Brunton, Ananda Pascual, Bertrand Chapron, Fabrice Collard, Lucile Gaultier, Ronan Fablet
Physic Informed Unknown Dynamic Nonlinear Prediction Norm Bounded Physic Constrained Atmospheric Dynamic Dynamic Embeddings Geophysical Field

February 7, 2022

Unsupervised physics-informed disentanglement of multimodal data for high-throughput scientific discovery
Nathaniel Trask, Carianne Martinez, Kookjin Lee, Brad Boyce
Variational Inference Multimodal Data Scientific Discovery Unsupervised Setting Physic Informed Modal Representation Cross Modal Generative

January 28, 2022

Discovering Nonlinear PDEs from Scarce Data with Physics-encoded Learning
Chengping Rao, Pu Ren, Yang Liu, Hao Sun
Partial Differential Equation Physic Informed Scarce Data

January 26, 2022

Physics-informed ConvNet: Learning Physical Field from a Shallow Neural Network
Pengpeng Shi, Zhi Zeng, Tianshou Liang
Physic Informed Machine Learning Physic Informed Shallow Neural Network Fully Connected Neural Network Shallow Layer Physical Law Learning Nonlinear Operator Mapping Wave Speed

January 22, 2022

Physics-Aware Safety-Assured Design of Hierarchical Neural Network based Planner
Xiangguo Liu, Chao Huang, Yixuan Wang, Bowen Zheng, Qi Zhu
Neural Network Autonomous Driving Physic Informed Hierarchical Planning Visual Planner

January 10, 2022

Desynchronous Learning in a Physics-Driven Learning Network
Jacob F Wycoff, Sam Dillavou, Menachem Stern, Andrea J Liu, Douglas J Durian
Decentralized Learning Physic Informed Asynchronous Learning

December 20, 2021

Learning Physics Properties of Fabrics and Garments with a Physics Similarity Neural Network
Li Duan, Lewis Boyd, Gerardo Aragon-Camarasa
Physic Informed Neural Network Physic Informed Geometric Fabric Physical Parameter

Physic Informed

Papers

Data and Physics Driven Learning Models for Fast MRI -- Fundamentals and Methodologies from CNN, GAN to Attention and Transformers

PI-VAE: Physics-Informed Variational Auto-Encoder for stochastic differential equations

Physics Informed RNN-DCT Networks for Time-Dependent Partial Differential Equations

The efficacy and generalizability of conditional GANs for posterior inference in physics-based inverse problems

Bounded nonlinear forecasts of partially observed geophysical systems with physics-constrained deep learning

Unsupervised physics-informed disentanglement of multimodal data for high-throughput scientific discovery

Discovering Nonlinear PDEs from Scarce Data with Physics-encoded Learning

Physics-informed ConvNet: Learning Physical Field from a Shallow Neural Network

Physics-Aware Safety-Assured Design of Hierarchical Neural Network based Planner

Desynchronous Learning in a Physics-Driven Learning Network

Learning Physics Properties of Fabrics and Garments with a Physics Similarity Neural Network