Cover Image

Approximating Families of Sharp Solutions to Fisher's Equation with Physics-Informed Neural Networks

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Rohrhofer, Franz M., Posch, Stefan, Gößnitzer, Clemens, Geiger, Bernhard C.
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Data vs. Physics: The Apparent Pareto Front of Physics-Informed Neural Networks
by: Rohrhofer, Franz M., et al.
Published: (2021)

Differentiable Chemistry in PINNs for Solving Parameterized and Stiff Reaction Systems
by: Babić, Miloš, et al.
Published: (2026)

B-PL-PINN: Stabilizing PINN Training with Bayesian Pseudo Labeling
by: Innerebner, Kevin, et al.
Published: (2025)

Stabilizing PINNs: A regularization scheme for PINN training to avoid unstable fixed points of dynamical systems
by: Babic, Milos, et al.
Published: (2025)

Information Plane Analysis of Binary Neural Networks
by: Nothnagel, Maximilian, et al.
Published: (2026)

Activation Bottleneck: Sigmoidal Neural Networks Cannot Forecast a Straight Line
by: Toller, Maximilian, et al.
Published: (2024)

General-Kindred Physics-Informed Neural Network to the Solutions of Singularly Perturbed Differential Equations
by: Wang, Sen, et al.
Published: (2024)

PinnDE: Physics-Informed Neural Networks for Solving Differential Equations
by: Matthews, Jason, et al.
Published: (2024)

Applications and Manipulations of Physics-Informed Neural Networks in Solving Differential Equations
by: Gupta, Aarush, et al.
Published: (2025)

Kronecker-Factored Approximate Curvature for Physics-Informed Neural Networks
by: Dangel, Felix, et al.
Published: (2024)

Verifying Physics-Informed Neural Network Fidelity using Classical Fisher Information from Differentiable Dynamical System
by: Filho, Josafat Ribeiro Leal, et al.
Published: (2026)

Transformed Physics-Informed Neural Networks for The Convection-Diffusion Equation
by: Guan, Jiajing, et al.
Published: (2024)

On the Role of Priors in Bayesian Causal Learning
by: Geiger, Bernhard C., et al.
Published: (2025)

Verifiable Error Bounds for Physics-Informed Neural Network Solutions of Lyapunov and Hamilton-Jacobi-Bellman Equations
by: Liu, Jun
Published: (2026)

Advancing Solutions for the Three-Body Problem Through Physics-Informed Neural Networks
by: Pereira, Manuel Santos, et al.
Published: (2025)

Physics-Informed Neural Networks for Joint Source and Parameter Estimation in Advection-Diffusion Equations
by: Anague, Brenda, et al.
Published: (2025)

A Family of Adaptive Activation Functions for Mitigating Failure Modes in Physics-Informed Neural Networks
by: Murari, Krishna
Published: (2026)

Taylor-Model Physics-Informed Neural Networks (PINNs) for Ordinary Differential Equations
by: Nagesh, Chandra Kanth, et al.
Published: (2025)

Correctness Verification of Neural Networks Approximating Differential Equations
by: Ellinas, Petros, et al.
Published: (2024)

Physics-Informed Neural Networks with Unknown Partial Differential Equations: an Application in Multivariate Time Series
by: Mortezanejad, Seyedeh Azadeh Fallah, et al.
Published: (2025)

NewPINNs: Physics-Informing Neural Networks Using Conventional Solvers for Partial Differential Equations
by: Makki, Maedeh, et al.
Published: (2026)

An Invitation to Deep Reinforcement Learning
by: Jaeger, Bernhard, et al.
Published: (2023)

Conformalized Physics-Informed Neural Networks
by: Podina, Lena, et al.
Published: (2024)

Physics-Informed Neural Networks for Control of Single-Phase Flow Systems Governed by Partial Differential Equations
by: Miyatake, Luis Kin, et al.
Published: (2025)

Trustworthy Representation Learning via Information Funnels and Bottlenecks
by: de Freitas, João Machado, et al.
Published: (2022)

Physics-Informed Neural Network for Predicting Out-of-Training-Range TCAD Solution with Minimized Domain Expertise
by: Lu, Albert, et al.
Published: (2024)

Meta-Inverse Physics-Informed Neural Networks for High-Dimensional Ordinary Differential Equations
by: Wei, Zhao, et al.
Published: (2026)

Fishers for Free? Approximating the Fisher Information Matrix by Recycling the Squared Gradient Accumulator
by: Li, YuXin, et al.
Published: (2025)

Physics-Informed Laplace Neural Operator for Solving Partial Differential Equations
by: Kim, Heechang, et al.
Published: (2026)

Approximating Simple ReLU Networks based on Spectral Decomposition of Fisher Information
by: Ho, Ka Long Keith, et al.
Published: (2025)

Energy Dissipation Preserving Physics Informed Neural Network for Allen-Cahn Equations
by: Kütük, Mustafa, et al.
Published: (2024)

Generalization Bounds for Physics-Informed Neural Networks for the Incompressible Navier-Stokes Equations
by: Andre-Sloan, Sebastien, et al.
Published: (2026)

Quantum-Classical Physics-Informed Neural Networks for Solving Reservoir Seepage Equations
by: Rao, Xiang, et al.
Published: (2025)

Deep Neural Network Solutions for Oscillatory Fredholm Integral Equations
by: Jiang, Jie, et al.
Published: (2024)

Physics-Informed Neural Network Frameworks for the Analysis of Engineering and Biological Dynamical Systems Governed by Ordinary Differential Equations
by: Whitman, Tyrus, et al.
Published: (2025)

Newton Informed Neural Operator for Computing Multiple Solutions of Nonlinear Partials Differential Equations
by: Hao, Wenrui, et al.
Published: (2024)

Spectrum-Informed Multistage Neural Networks: Multiscale Function Approximators of Machine Precision
by: Ng, Jakin, et al.
Published: (2024)

Riemannian Laplace Approximation with the Fisher Metric
by: Yu, Hanlin, et al.
Published: (2023)

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations
by: Farea, Afrah, et al.
Published: (2024)

Data-Guided Physics-Informed Neural Networks for Solving Inverse Problems in Partial Differential Equations
by: Zhou, Wei, et al.
Published: (2024)