TINNs: Time-Induced Neural Networks for Solving Time-Dependent PDEs
Fuente:
arXiv
Saved in:
| Main Authors: | Dai, Chen-Yang, Chang, Che-Chia, Lin, Te-Sheng, Lai, Ming-Chih, Lai, Chieh-Hsin |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Stabilizing Physics-Informed Consistency Models via Structure-Preserving Training
by: Chang, Che-Chia, et al.
Published: (2026)
by: Chang, Che-Chia, et al.
Published: (2026)
A hybrid neural-network and MAC scheme for Stokes interface problems
by: Chang, Che-Chia, et al.
Published: (2023)
by: Chang, Che-Chia, et al.
Published: (2023)
Latent Neural Operator Pretraining for Solving Time-Dependent PDEs
by: Wang, Tian, et al.
Published: (2024)
by: Wang, Tian, et al.
Published: (2024)
A categorical embedding discontinuity-capturing shallow neural network for anisotropic elliptic interface problems
by: Hu, Wei-Fan, et al.
Published: (2025)
by: Hu, Wei-Fan, et al.
Published: (2025)
A shallow physics-informed neural network for solving partial differential equations on surfaces
by: Hu, Wei-Fan, et al.
Published: (2022)
by: Hu, Wei-Fan, et al.
Published: (2022)
Autoregression-Free Neural Operators for Time-Dependent PDEs
by: Zhang, Jiaquan, et al.
Published: (2026)
by: Zhang, Jiaquan, et al.
Published: (2026)
Consistency Training with Physical Constraints
by: Chang, Che-Chia, et al.
Published: (2025)
by: Chang, Che-Chia, et al.
Published: (2025)
Solving Roughly Forced Nonlinear PDEs via Misspecified Kernel Methods and Neural Networks
by: Baptista, Ricardo, et al.
Published: (2025)
by: Baptista, Ricardo, et al.
Published: (2025)
TENG: Time-Evolving Natural Gradient for Solving PDEs With Deep Neural Nets Toward Machine Precision
by: Chen, Zhuo, et al.
Published: (2024)
by: Chen, Zhuo, et al.
Published: (2024)
Solving PDEs on Spheres with Physics-Informed Convolutional Neural Networks
by: Lei, Guanhang, et al.
Published: (2023)
by: Lei, Guanhang, et al.
Published: (2023)
Solving PDEs on Unknown Manifolds with Machine Learning
by: Liang, Senwei, et al.
Published: (2021)
by: Liang, Senwei, et al.
Published: (2021)
Solving Nonlinear PDEs with Sparse Radial Basis Function Networks
by: Shao, Zihan, et al.
Published: (2025)
by: Shao, Zihan, et al.
Published: (2025)
A Unified Weighted-Loss Physics-Informed Neural Network for Boundary Layer Problems in Singularly Perturbed PDEs
by: Hu, Wei-Fan, et al.
Published: (2026)
by: Hu, Wei-Fan, et al.
Published: (2026)
Graph-based Semi-supervised Local Clustering with Few Labeled Nodes
by: Shen, Zhaiming, et al.
Published: (2022)
by: Shen, Zhaiming, et al.
Published: (2022)
Automatic Differentiation is Essential in Training Neural Networks for Solving Differential Equations
by: Chen, Chuqi, et al.
Published: (2024)
by: Chen, Chuqi, et al.
Published: (2024)
Predictive Moving Sample Method for Physics-Informed Neural Solvers of Time-Dependent PDEs
by: Xu, Beining, et al.
Published: (2026)
by: Xu, Beining, et al.
Published: (2026)
Stochastic Quadrature Rules for Solving PDEs using Neural Networks
by: Taylor, Jamie M., et al.
Published: (2025)
by: Taylor, Jamie M., et al.
Published: (2025)
Solving High-Dimensional PDEs Using Linearized Neural Networks
by: Mao, Tong, et al.
Published: (2026)
by: Mao, Tong, et al.
Published: (2026)
Finite Element Neural Network Interpolation. Part I: Interpretable and Adaptive Discretization for Solving PDEs
by: Škardová, Kateřina, et al.
Published: (2024)
by: Škardová, Kateřina, et al.
Published: (2024)
DDC-PINNs: A Predictor-Corrector Approach Based on Neural Network-Driven Domain Decomposition and Classical ODE Solvers for Time-Dependent PDEs
by: Yang, Xun, et al.
Published: (2025)
by: Yang, Xun, et al.
Published: (2025)
Quantum Recurrent Neural Networks with Encoder-Decoder for Time-Dependent Partial Differential Equations
by: Chen, Yuan, et al.
Published: (2025)
by: Chen, Yuan, et al.
Published: (2025)
Solving PDEs With Deep Neural Nets under General Boundary Conditions
by: Zhang, Chenggong
Published: (2025)
by: Zhang, Chenggong
Published: (2025)
Adaptive-Distribution Randomized Neural Networks for PDEs: A Low-Dimensional Distribution-Learning Framework
by: Yang, You, et al.
Published: (2026)
by: Yang, You, et al.
Published: (2026)
A Quantum Spectral Framework for Solving PDEs
by: Huang, Chih-Kang, et al.
Published: (2026)
by: Huang, Chih-Kang, et al.
Published: (2026)
Approximation Theory and Applications of Randomized Neural Networks for Solving High-Dimensional PDEs
by: De Ryck, T., et al.
Published: (2025)
by: De Ryck, T., et al.
Published: (2025)
Supervised and Unsupervised Neural Network Solver for First Order Hyperbolic Nonlinear PDEs
by: Baba, Zakaria, et al.
Published: (2026)
by: Baba, Zakaria, et al.
Published: (2026)
RELift: Learned Coarse-to-Fine Propagators for Time-Dependent PDEs with Applications to Electron Dynamics
by: Bassi, Hardeep, et al.
Published: (2025)
by: Bassi, Hardeep, et al.
Published: (2025)
Physics-Informed Machine Learning for Two-Phase Moving-Interface and Stefan Problems
by: Chang, Che-Chia, et al.
Published: (2025)
by: Chang, Che-Chia, et al.
Published: (2025)
Parallel-in-Time Solutions with Random Projection Neural Networks
by: Betcke, Marta M., et al.
Published: (2024)
by: Betcke, Marta M., et al.
Published: (2024)
What Can One Expect When Solving PDEs Using Shallow Neural Networks?
by: He, Roy Y., et al.
Published: (2025)
by: He, Roy Y., et al.
Published: (2025)
Neural Measures for learning distributions of Random PDEs
by: Arampatzis, Georgios, et al.
Published: (2025)
by: Arampatzis, Georgios, et al.
Published: (2025)
Enhanced BPINN Training Convergence in Solving General and Multi-scale Elliptic PDEs with Noise
by: Hou, Yilong, et al.
Published: (2024)
by: Hou, Yilong, et al.
Published: (2024)
Parameterized Physics-informed Neural Networks for Parameterized PDEs
by: Cho, Woojin, et al.
Published: (2024)
by: Cho, Woojin, et al.
Published: (2024)
Maximal Volume Matrix Cross Approximation for Image Compression and Least Squares Solution
by: Allen, Kenneth, et al.
Published: (2023)
by: Allen, Kenneth, et al.
Published: (2023)
Solving Elliptic Optimal Control Problems via Neural Networks and Optimality System
by: Dai, Yongcheng, et al.
Published: (2023)
by: Dai, Yongcheng, et al.
Published: (2023)
FastLSQ: Solving PDEs in One Shot via Fourier Features with Exact Analytical Derivatives
by: Sulc, Antonin
Published: (2026)
by: Sulc, Antonin
Published: (2026)
Semi-Discrete in Time Method for Time-Dependent Equations by Random Neural Basis
by: Wang, Guihong, et al.
Published: (2025)
by: Wang, Guihong, et al.
Published: (2025)
Learn to Evolve: Self-supervised Neural JKO Operator for Wasserstein Gradient Flow
by: Feng, Xue, et al.
Published: (2026)
by: Feng, Xue, et al.
Published: (2026)
Fourier Neural Operator with Learned Deformations for PDEs on General Geometries
by: Li, Zongyi, et al.
Published: (2022)
by: Li, Zongyi, et al.
Published: (2022)
Monte Carlo Neural PDE Solver for Learning PDEs via Probabilistic Representation
by: Zhang, Rui, et al.
Published: (2023)
by: Zhang, Rui, et al.
Published: (2023)
Similar Items
-
Stabilizing Physics-Informed Consistency Models via Structure-Preserving Training
by: Chang, Che-Chia, et al.
Published: (2026) -
A hybrid neural-network and MAC scheme for Stokes interface problems
by: Chang, Che-Chia, et al.
Published: (2023) -
Latent Neural Operator Pretraining for Solving Time-Dependent PDEs
by: Wang, Tian, et al.
Published: (2024) -
A categorical embedding discontinuity-capturing shallow neural network for anisotropic elliptic interface problems
by: Hu, Wei-Fan, et al.
Published: (2025) -
A shallow physics-informed neural network for solving partial differential equations on surfaces
by: Hu, Wei-Fan, et al.
Published: (2022)