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physics-informed neural network

97 papers

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Also known as

PINNS PINN

Co-occurring keywords

partial differential equation (222) neural network optimization (1293) inductive bia (272) differential equation (56) dynamical system (398) neural ordinary differential equation (139) operator learning (48) ordinary differential equation (139) gradient descent (1144) automatic differentiation (77)

Papers

Stochastic Taylor Derivative Estimator: Efficient amortization for arbitrary differential operators NIPS 2024
DMIS: Dynamic Mesh-Based Importance Sampling for Training Physics-Informed Neural Networks AAAI 2023
Random Grid Neural Processes for Parametric Partial Differential Equations ICML 2023
A Continuous-time Stochastic Gradient Descent Method for Continuous Data JMLR 2023
Exploration on Physics-Informed Neural Networks on Partial Differential Equations (Student Abstract) AAAI 2023
MultiAdam: Parameter-wise Scale-invariant Optimizer for Multiscale Training of Physics-informed Neural Networks ICML 2023
Universal Physics-Informed Neural Networks: Symbolic Differential Operator Discovery with Sparse Data ICML 2023
Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks ICML 2023
Physics-Informed Model-Based Reinforcement Learning L4DC 2023
PIXEL: Physics-Informed Cell Representations for Fast and Accurate PDE Solvers AAAI 2023
EINNs: Epidemiologically-Informed Neural Networks AAAI 2023
Hypernetwork-based Meta-Learning for Low-Rank Physics-Informed Neural Networks NIPS 2023
A Generalizable Physics-informed Learning Framework for Risk Probability Estimation L4DC 2023
CS4ML: A general framework for active learning with arbitrary data based on Christoffel functions NIPS 2023
Harmonic Neural Networks ICML 2023
Mitigating Propagation Failures in Physics-informed Neural Networks using Retain-Resample-Release (R3) Sampling ICML 2023
Separable Physics-Informed Neural Networks NIPS 2023
Lie Point Symmetry and Physics-Informed Networks NIPS 2023
How to Learn and Generalize From Three Minutes of Data: Physics-Constrained and Uncertainty-Aware Neural Stochastic Differential Equations CORL 2023
Achieving High Accuracy with PINNs via Energy Natural Gradient Descent ICML 2023
Gradient Descent Finds the Global Optima of Two-Layer Physics-Informed Neural Networks ICML 2023
Implicit Stochastic Gradient Descent for Training Physics-Informed Neural Networks AAAI 2023
Learning Physics-Informed Neural Networks without Stacked Back-propagation AISTATS 2023
Residual-based error bound for physics-informed neural networks UAI 2023
Progressive Learning for Physics-informed Neural Motion Planning RSS 2023