Forecasting of spatiotemporal nonlinear dynamic systems by Physics-informed neural networks with ResNet blocks

No abstract is available for this paper.

• Publication year

  2026

• Venue

  Physica A: Statistical Mechanics and its Applications

• Publication date

  2026-01-01

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1016/j.physd.2025.135040
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Chaotic climate system forecasting using an improved echo state network with sparse observations

  2025cited by this paper
• Data analysis of dynamical system for the optimization of disease dynamics through Neural Networks Paradigm

  2025cited by this paper
• Forecasting chaotic time series: Comparative performance of LSTM-based and Transformer-based neural network

  2025cited by this paper
• Synchronization and chimeras in asymmetrically coupled memristive Tabu learning neuron network

  2025cited by this paper
• Transformer-based deep learning architecture for time series forecasting

  2024cited by this paper
• PirateNets: Physics-informed Deep Learning with Residual Adaptive Networks

  2024cited by this paper
• Numerical investigation of heat and mass transfer in micropolar nanofluid flows over an inclined surface with stochastic numerical approach

  2024cited by this paper
• Using a snow ablation optimizer in an autonomous echo state network for the model-free prediction of chaotic systems

  2024cited by this paper
• Respecting causality for training physics-informed neural networks

  2024cited by this paper
• Constrained Self-Adaptive Physics-Informed Neural Networks with ResNet Block-Enhanced Network Architecture

  2023cited by this paper
• Reproducing Activation Function for Deep Learning

  2021cited by this paper
• Characterizing possible failure modes in physics-informed neural networks

  2021cited by this paper
• Exact imposition of boundary conditions with distance functions in physics-informed deep neural networks

  2021cited by this paper
• Efficient training of physics‐informed neural networks via importance sampling

  2021cited by this paper
• A Physics Informed Neural Network for Time-Dependent Nonlinear and Higher Order Partial Differential Equations

  2021cited by this paper
• Deep Learning Method Based on Physics Informed Neural Network with Resnet Block for Solving Fluid Flow Problems

  2021cited by this paper
• Deep Kronecker neural networks: A general framework for neural networks with adaptive activation functions

  2021cited by this paper
• High-efficiency chaotic time series prediction based on time convolution neural network

  2021cited by this paper
• Finite basis physics-informed neural networks (FBPINNs): a scalable domain decomposition approach for solving differential equations

  2021cited by this paper
• Physics-informed machine learning

  2021cited by this paper
• Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks

  2021cited by this paper
• Estimates on the generalization error of physics-informed neural networks for approximating a class of inverse problems for PDEs

  2020cited by this paper
• Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations

  2020cited by this paper
• B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data

  2020cited by this paper
• Solving Allen-Cahn and Cahn-Hilliard Equations using the Adaptive Physics Informed Neural Networks

  2020cited by this paper
• A Method for Representing Periodic Functions and Enforcing Exactly Periodic Boundary Conditions with Deep Neural Networks

  2020cited by this paper
• When and why PINNs fail to train: A neural tangent kernel perspective

  2020cited by this paper
• Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism

  2020cited by this paper
• Extended Physics-informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition based Deep Learning Framework for Nonlinear Partial Differential Equations

  2020cited by this paper
• On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks

  2020cited by this paper
• PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain

  2020cited by this paper
• Uncovering turbulent plasma dynamics via deep learning from partial observations.

  2020cited by this paper
• Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

  2019cited by this paper
• Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators

  2019cited by this paper
• Systems biology informed deep learning for inferring parameters and hidden dynamics

  2019cited by this paper
• Machine learning in cardiovascular flows modeling: Predicting pulse wave propagation from non-invasive clinical measurements using physics-informed deep learning

  2019cited by this paper
• Adaptive activation functions accelerate convergence in deep and physics-informed neural networks

  2019cited by this paper
• Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations

  2018cited by this paper
• Deep Residual Learning for Image Recognition

  2015cited by this paper
• Adam: A Method for Stochastic Optimization

  2014cited by this paper
• Understanding the difficulty of training deep feedforward neural networks

  2010cited by this paper
• Evaluating derivatives - principles and techniques of algorithmic differentiation, Second Edition

  2000cited by this paper
• Nonlinear analysis of hydrodynamic instability in laminar flames—I. Derivation of basic equations

  1977cited by this paper
• Persistent Propagation of Concentration Waves in Dissipative Media Far from Thermal Equilibrium

  1976cited by this paper
• Deterministic nonperiodic flow

  1963cited by this paper

• No citing papers are available for this paper.