:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Costabal, Francisco Sahli, Pezzuto, Simone, Perdikaris, Paris
Format:	Preprint
Published:	2022
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2209.03984
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Ensemble learning of the atrial fiber orientation with physics-informed neural networks
by: Magaña, Efraín, et al.
Published: (2024)

WarpPINN-fibers: improved cardiac strain estimation from cine-MR with physics-informed neural networks
by: Barrientos, Felipe Álvarez, et al.
Published: (2025)

Understanding the dynamics of the frequency bias in neural networks
by: Molina, Juan, et al.
Published: (2024)

When PINNs Go Wrong: Pseudo-Time Stepping Against Spurious Solutions
by: Wang, Sifan, et al.
Published: (2026)

An eikonal model with re-excitability for fast simulations in cardiac electrophysiology
by: Gander, Lia, et al.
Published: (2024)

Fully data-driven inverse hyperelasticity with hyper-network neural ODE fields
by: Taç, Vahidullah, et al.
Published: (2025)

PhysicsCorrect: A Training-Free Approach for Stable Neural PDE Simulations
by: Huang, Xinquan, et al.
Published: (2025)

PirateNets: Physics-informed Deep Learning with Residual Adaptive Networks
by: Wang, Sifan, et al.
Published: (2024)

Randomness and signal propagation in physics-informed neural networks (PINNs): A neural PDE perspective
by: Tucny, Jean-Michel, et al.
Published: (2025)

Self-Flow-Matching assisted Full Waveform Inversion
by: Huang, Xinquan, et al.
Published: (2026)

Enhanced physics-informed neural networks (PINNs) for high-order power grid dynamics
by: Nair, Vineet Jagadeesan
Published: (2024)

Uncertainty Quantification for Cardiac Shape Reconstruction with Deep Signed Distance Functions via MCMC methods
by: Verhülsdonk, Jan, et al.
Published: (2026)

Do physics-informed neural networks (PINNs) need to be deep? Shallow PINNs using the Levenberg-Marquardt algorithm
by: Shahab, Muhammad Luthfi, et al.
Published: (2026)

Gradient Alignment in Physics-informed Neural Networks: A Second-Order Optimization Perspective
by: Wang, Sifan, et al.
Published: (2025)

Ensemble learning for Physics Informed Neural Networks: a Gradient Boosting approach
by: Fang, Zhiwei, et al.
Published: (2023)

CFO: Learning Continuous-Time PDE Dynamics via Flow-Matched Neural Operators
by: Hou, Xianglong, et al.
Published: (2025)

Small Models, Strong Priors: Architectural Inductive Bias for Parameter-Efficient Neural PDE Solvers
by: Sankaran, Shyam, et al.
Published: (2026)

Sharp-PINNs: staggered hard-constrained physics-informed neural networks for phase field modelling of corrosion
by: Chen, Nanxi, et al.
Published: (2025)

Simulating Three-dimensional Turbulence with Physics-informed Neural Networks
by: Wang, Sifan, et al.
Published: (2025)

Physics-informed neural networks (PINNs) for numerical model error approximation and superresolution
by: Zhuang, Bozhou, et al.
Published: (2024)

Composite Bayesian Optimization In Function Spaces Using NEON -- Neural Epistemic Operator Networks
by: Guilhoto, Leonardo Ferreira, et al.
Published: (2024)

Deep Learning Alternatives of the Kolmogorov Superposition Theorem
by: Guilhoto, Leonardo Ferreira, et al.
Published: (2024)

HomPINNs: homotopy physics-informed neural networks for solving the inverse problems of nonlinear differential equations with multiple solutions
by: Zheng, Haoyang, et al.
Published: (2023)

Physics-Informed Neural Networks and Extensions
by: Raissi, Maziar, et al.
Published: (2024)

Decomposing stimulus-specific sensory neural information via diffusion models
by: Laquitaine, Steeve, et al.
Published: (2025)

Learning geometry-dependent lead-field operators for forward ECG modeling
by: Dokuchaev, Arsenii, et al.
Published: (2026)

Score Neural Operator: A Generative Model for Learning and Generalizing Across Multiple Probability Distributions
by: Liao, Xinyu, et al.
Published: (2024)

Image Velocimetry using Direct Displacement Field estimation with Neural Networks for Fluids
by: Magaña, Efraín, et al.
Published: (2025)

Enforcing hidden physics in physics-informed neural networks
by: Chen, Nanxi, et al.
Published: (2025)

Visualizing the loss landscapes of physics-informed neural networks
by: Rowan, Conor, et al.
Published: (2026)

BO-SA-PINNs: Self-adaptive physics-informed neural networks based on Bayesian optimization for automatically designing PDE solvers
by: Zhang, Rui, et al.
Published: (2025)

Micrometer: Micromechanics Transformer for Predicting Mechanical Responses of Heterogeneous Materials
by: Wang, Sifan, et al.
Published: (2024)

Multimodal Scientific Learning Beyond Diffusions and Flows
by: Guilhoto, Leonardo Ferreira, et al.
Published: (2026)

Higher-order-ReLU-KANs (HRKANs) for solving physics-informed neural networks (PINNs) more accurately, robustly and faster
by: So, Chi Chiu, et al.
Published: (2024)

Graph neural networks informed locally by thermodynamics
by: Tierz, Alicia, et al.
Published: (2024)

A Mutual Information Lower Bound for Multimodal Regression Active Learning
by: Guilhoto, Leonardo Ferreira, et al.
Published: (2026)

Strategies for training point distributions in physics-informed neural networks
by: Humagain, Santosh, et al.
Published: (2025)

When do complex-valued neural networks help? A study of representation, geometry, and optimization
by: Kumar, Ashutosh
Published: (2026)

Hybrid quantum physics-informed neural networks for simulating computational fluid dynamics in complex shapes
by: Sedykh, Alexandr, et al.
Published: (2023)

Pseudo-differential-enhanced physics-informed neural networks
by: Gracyk, Andrew
Published: (2026)