Imagen de Portada

Revising the Structure of Recurrent Neural Networks to Eliminate Numerical Derivatives in Forming Physics Informed Loss Terms with Respect to Time

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Jahani-nasab, Mahyar, Bijarchi, Mohamad Ali
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Enhancing Convergence Speed with Feature-Enforcing Physics-Informed Neural Networks: Utilizing Boundary Conditions as Prior Knowledge for Faster Convergence
por: Jahaninasab, Mahyar, et al.
Publicado: (2023)

Intelligent Optimization of Multi-Parameter Micromixers Using a Scientific Machine Learning Framework
por: Hassanzadeh, Meraj, et al.
Publicado: (2025)

Energy-Efficient Spiking Recurrent Neural Network for Gesture Recognition on Embedded GPUs
por: Varposhti, Marzieh Hassanshahi, et al.
Publicado: (2024)

FlexPINN: Modeling Fluid Dynamics and Mass Transfer in 3D Micromixer Geometries Using a Flexible Physics-Informed Neural Network
por: Hassanzadeh, Meraj, et al.
Publicado: (2025)

Provable Bounds on the Hessian of Neural Networks: Derivative-Preserving Reachability Analysis
por: Sharifi, Sina, et al.
Publicado: (2024)

Model Based and Physics Informed Deep Learning Neural Network Structures
por: Mohammad-Djafari, Ali, et al.
Publicado: (2024)

Per-Loss Adapters for Gradient Conflict in Physics-Informed Neural Networks
por: Kim, Bum Jun, et al.
Publicado: (2026)

Physics-Informed Neural Networks: Minimizing Residual Loss with Wide Networks and Effective Activations
por: Dashtbayaz, Nima Hosseini, et al.
Publicado: (2024)

Exploring Physics-Informed Neural Networks for Crop Yield Loss Forecasting
por: Miranda, Miro, et al.
Publicado: (2024)

Physics-Informed Neural Networks with Learnable Loss Balancing and Transfer Learning
por: Pirayeshshirazinezhad, Reza
Publicado: (2026)

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

Loss Terms and Operator Forms of Koopman Autoencoders
por: Enyeart, Dustin, et al.
Publicado: (2024)

Structure-Preserving Physics-Informed Neural Networks With Energy or Lyapunov Structure
por: Chu, Haoyu, et al.
Publicado: (2024)

Physics-Informed Neural Networks for Solving Derivative-Constrained PDEs
por: Hoshisashi, Kentaro, et al.
Publicado: (2026)

Learning from Integral Losses in Physics Informed Neural Networks
por: Saleh, Ehsan, et al.
Publicado: (2023)

Neural Tangent Kernel of Neural Networks with Loss Informed by Differential Operators
por: Gan, Weiye, et al.
Publicado: (2025)

Bayesian Physics Informed Neural Networks for Linear Inverse problems
por: Mohammad-Djafari, Ali
Publicado: (2025)

Continuous-Time Piecewise-Linear Recurrent Neural Networks
por: Brändle, Alena, et al.
Publicado: (2026)

Time-Warping Recurrent Neural Networks for Transfer Learning
por: Hirschi, Jonathon
Publicado: (2026)

Equation Discovery, Parametric Simulation, and Optimization Using the Physics-Informed Neural Network (PINN) Method for the Heat Conduction Problem
por: Ghaderi, Ehsan, et al.
Publicado: (2025)

Physics-Informed Neural Networks for Shell Structures
por: Bastek, Jan-Hendrik, et al.
Publicado: (2022)

Recurrent Neural Networks with Linear Structures for Electricity Price Forecasting
por: Amor, Souhir Ben, et al.
Publicado: (2025)

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

Impact of Loss Weight and Model Complexity on Physics-Informed Neural Networks for Computational Fluid Dynamics
por: Chou, Yi En, et al.
Publicado: (2025)

Neural Operator Learning for Long-Time Integration in Dynamical Systems with Recurrent Neural Networks
por: Michałowska, Katarzyna, et al.
Publicado: (2023)

Deep State Space Recurrent Neural Networks for Time Series Forecasting
por: Inzirillo, Hugo
Publicado: (2024)

SGM-PINN: Sampling Graphical Models for Faster Training of Physics-Informed Neural Networks
por: Anticev, John, et al.
Publicado: (2024)

Numerical simulation of transient heat conduction with moving heat source using Physics Informed Neural Networks
por: Kalyan, Anirudh, et al.
Publicado: (2025)

Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Forming
por: Zhao, Yingxue, et al.
Publicado: (2025)

Integrating Biological-Informed Recurrent Neural Networks for Glucose-Insulin Dynamics Modeling
por: De Carli, Stefano, et al.
Publicado: (2025)

Accelerating Long-Term Molecular Dynamics with Physics-Informed Time-Series Forecasting
por: Le, Hung, et al.
Publicado: (2025)

Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs
por: Huo, Wenxuan, et al.
Publicado: (2025)

RWKV-TS: Beyond Traditional Recurrent Neural Network for Time Series Tasks
por: Hou, Haowen, et al.
Publicado: (2024)

Fourier-Enhanced Recurrent Neural Networks for Electrical Load Time Series Downscaling
por: Chen, Qi, et al.
Publicado: (2025)

ParaRNN: An Interpretable and Parallelizable Recurrent Neural Network for Time-Dependent Data
por: Cai, Yuxi, et al.
Publicado: (2026)

Investigating Sparsity in Recurrent Neural Networks
por: Darji, Harshil
Publicado: (2024)

Bayesian Physics-Informed Neural Networks for Inverse Problems (BPINN-IP): Application in Infrared Image Processing
por: Mohammad-Djafari, Ali, et al.
Publicado: (2025)

Spectral Pruning for Recurrent Neural Networks
por: Furuya, Takashi, et al.
Publicado: (2021)

AB-PINNs: Adaptive-Basis Physics-Informed Neural Networks for Residual-Driven Domain Decomposition
por: Botvinick-Greenhouse, Jonah, et al.
Publicado: (2025)

Adaptive Structured Pruning of Convolutional Neural Networks for Time Series Classification
por: Abdullayev, Javidan, et al.
Publicado: (2026)