Physics-informed neural networks with hard constraints for inverse design

9 February 2021

Papers citing "Physics-informed neural networks with hard constraints for inverse design"

7 / 7 papers shown

Title
Reliable and Efficient Inverse Analysis using Physics-Informed Neural Networks with Distance Functions and Adaptive Weight Tuning Shota Deguchi Mitsuteru Asai PINN AI4CE 66 0 0 25 Apr 2025
A Probabilistic Neuro-symbolic Layer for Algebraic Constraint Satisfaction Leander Kurscheidt Paolo Morettin Roberto Sebastiani Andrea Passerini Antonio Vergari 44 0 0 25 Mar 2025
Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks Cyrus Neary Nathan Tsao Ufuk Topcu 67 1 0 15 Dec 2024
Stochastic Taylor Derivative Estimator: Efficient amortization for arbitrary differential operators Zekun Shi Zheyuan Hu Min-Bin Lin Kenji Kawaguchi 89 4 0 27 Nov 2024
Fourier PINNs: From Strong Boundary Conditions to Adaptive Fourier Bases Madison Cooley Varun Shankar Robert M. Kirby Shandian Zhe 20 2 0 04 Oct 2024
Robust Biharmonic Skinning Using Geometric Fields Ana Dodik Vincent Sitzmann Justin Solomon Oded Stein 3DH 34 2 0 01 Jun 2024
Hybrid thermal modeling of additive manufacturing processes using physics-informed neural networks for temperature prediction and parameter identification Shuheng Liao Tianju Xue Jihoon Jeong Samantha Webster K. Ehmann Jian Cao AI4CE 15 46 0 15 Jun 2022