Data vs. Physics: The Apparent Pareto Front of Physics-Informed Neural
Networks

Data vs. Physics: The Apparent Pareto Front of Physics-Informed Neural Networks

3 May 2021

Franz M. Rohrhofer

Bernhard C. Geiger

Papers citing "Data vs. Physics: The Apparent Pareto Front of Physics-Informed Neural Networks"

18 / 18 papers shown

Title
Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models Matthias Höfler Francesco Regazzoni S. Pagani Elias Karabelas Christoph M. Augustin Gundolf Haase Gernot Plank Federica Caforio 22 0 0 06 May 2025
Reliable and Efficient Inverse Analysis using Physics-Informed Neural Networks with Distance Functions and Adaptive Weight Tuning Shota Deguchi Mitsuteru Asai PINN AI4CE 76 0 0 25 Apr 2025
PINNverse: Accurate parameter estimation in differential equations from noisy data with constrained physics-informed neural networks Marius Almanstötter Roman Vetter Dagmar Iber PINN 27 1 0 07 Apr 2025
Bringing Chemistry to Scale: Loss Weight Adjustment for Multivariate Regression in Deep Learning of Thermochemical Processes Franz M. Rohrhofer S. Posch C. Gößnitzer J. M. García-Oliver Bernhard C. Geiger 16 1 0 03 Aug 2023
PINNacle: A Comprehensive Benchmark of Physics-Informed Neural Networks for Solving PDEs Zhongkai Hao J. Yao Chang Su Hang Su Ziao Wang ... Zeyu Xia Yichi Zhang Songming Liu Lu Lu Jun Zhu PINN 29 29 0 15 Jun 2023
Error convergence and engineering-guided hyperparameter search of PINNs: towards optimized I-FENN performance Panos Pantidis Habiba Eldababy Christopher Miguel Tagle M. Mobasher 27 20 0 03 Mar 2023
Mixed formulation of physics-informed neural networks for thermo-mechanically coupled systems and heterogeneous domains Ali Harandi Ahmad Moeineddin Michael Kaliske Stefanie Reese Shahed Rezaei AI4CE PINN 18 42 0 09 Feb 2023
PINN Training using Biobjective Optimization: The Trade-off between Data Loss and Residual Loss Fabian Heldmann Sarah Treibert Matthias Ehrhardt K. Klamroth 24 20 0 03 Feb 2023
Neuroevolution of Physics-Informed Neural Nets: Benchmark Problems and Comparative Results Nicholas Sung Jian Cheng Wong C. Ooi Abhishek Gupta P. Chiu Yew-Soon Ong PINN 14 6 0 15 Dec 2022
Bayesian Physics Informed Neural Networks for Data Assimilation and Spatio-Temporal Modelling of Wildfires J. Dabrowski D. Pagendam J. Hilton Conrad Sanderson Dan MacKinlay C. Huston Andrew Bolt Petra Kuhnert PINN 25 16 0 02 Dec 2022
Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications Zhongkai Hao Songming Liu Yichi Zhang Chengyang Ying Yao Feng Hang Su Jun Zhu PINN AI4CE 21 87 0 15 Nov 2022
A connection between probability, physics and neural networks Sascha Ranftl PINN 15 9 0 26 Sep 2022
Enhanced Physics-Informed Neural Networks with Augmented Lagrangian Relaxation Method (AL-PINNs) Hwijae Son S. Cho H. Hwang PINN 19 41 0 29 Apr 2022
Calibrating constitutive models with full-field data via physics informed neural networks Craig M. Hamel K. Long S. Kramer AI4CE 25 28 0 30 Mar 2022
Structure-preserving Sparse Identification of Nonlinear Dynamics for Data-driven Modeling Kookjin Lee Nathaniel Trask P. Stinis 30 24 0 11 Sep 2021
Training multi-objective/multi-task collocation physics-informed neural network with student/teachers transfer learnings B. Bahmani WaiChing Sun PINN AI4CE 31 17 0 24 Jul 2021
Exact imposition of boundary conditions with distance functions in physics-informed deep neural networks N. Sukumar Ankit Srivastava PINN AI4CE 41 240 0 17 Apr 2021
Physics-informed neural networks with hard constraints for inverse design Lu Lu R. Pestourie Wenjie Yao Zhicheng Wang F. Verdugo Steven G. Johnson PINN 39 493 0 09 Feb 2021