Optimizing the Optimizer for Physics-Informed Neural Networks and Kolmogorov-Arnold Networks
Fuente:
arXiv
Saved in:
| Main Authors: | Kiyani, Elham, Shukla, Khemraj, Urbán, Jorge F., Darbon, Jérôme, Karniadakis, George Em |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Curvature-Aware Optimization for High-Accuracy Physics-Informed Neural Networks
by: Jnini, Anas, et al.
Published: (2026)
by: Jnini, Anas, et al.
Published: (2026)
HJ-sampler: A Bayesian sampler for inverse problems of a stochastic process by leveraging Hamilton-Jacobi PDEs and score-based generative models
by: Meng, Tingwei, et al.
Published: (2024)
by: Meng, Tingwei, et al.
Published: (2024)
On the Convergence of (Stochastic) Gradient Descent for Kolmogorov--Arnold Networks
by: Gao, Yihang, et al.
Published: (2024)
by: Gao, Yihang, et al.
Published: (2024)
Leveraging Hamilton-Jacobi PDEs with time-dependent Hamiltonians for continual scientific machine learning
by: Chen, Paula, et al.
Published: (2023)
by: Chen, Paula, et al.
Published: (2023)
PINNs in PDE Constrained Optimal Control Problems: Direct vs Indirect Methods
by: Zhang, Zhen, et al.
Published: (2026)
by: Zhang, Zhen, et al.
Published: (2026)
Tackling the Curse of Dimensionality with Physics-Informed Neural Networks
by: Hu, Zheyuan, et al.
Published: (2023)
by: Hu, Zheyuan, et al.
Published: (2023)
A Variational Framework for Residual-Based Adaptivity in Neural PDE Solvers and Operator Learning
by: Toscano, Juan Diego, et al.
Published: (2025)
by: Toscano, Juan Diego, et al.
Published: (2025)
GIMLET: Generalizable and Interpretable Model Learning through Embedded Thermodynamics
by: Shiratori, Suguru, et al.
Published: (2025)
by: Shiratori, Suguru, et al.
Published: (2025)
Scalable Bayesian Physics-Informed Kolmogorov-Arnold Networks
by: Gao, Zhiwei, et al.
Published: (2025)
by: Gao, Zhiwei, et al.
Published: (2025)
Randomized Forward Mode of Automatic Differentiation For Optimization Algorithms
by: Shukla, Khemraj, et al.
Published: (2023)
by: Shukla, Khemraj, et al.
Published: (2023)
PI-SONet: A Physics-Informed Symplectic Operator Network for Real-Time Optimal Control of Multi-Agent Systems
by: Varghese, Alan John, et al.
Published: (2026)
by: Varghese, Alan John, et al.
Published: (2026)
Bayesian Optimization for Hyperparameters Tuning in Neural Networks
by: Onorato, Gabriele
Published: (2024)
by: Onorato, Gabriele
Published: (2024)
Deep Operator Neural Network Model Predictive Control
by: de Jong, Thomas Oliver, et al.
Published: (2025)
by: de Jong, Thomas Oliver, et al.
Published: (2025)
Physics-Informed Neural Networks and Extensions
by: Raissi, Maziar, et al.
Published: (2024)
by: Raissi, Maziar, et al.
Published: (2024)
Solving Functional Optimization with Deep Networks and Variational Principles
by: Kamtue, Kawisorn, et al.
Published: (2024)
by: Kamtue, Kawisorn, et al.
Published: (2024)
Automatic discovery of optimal meta-solvers via multi-objective optimization
by: Lee, Youngkyu, et al.
Published: (2024)
by: Lee, Youngkyu, et al.
Published: (2024)
A Riemannian Optimization Perspective of the Gauss-Newton Method for Feedforward Neural Networks
by: Cayci, Semih
Published: (2024)
by: Cayci, Semih
Published: (2024)
Optimizing Inventory Routing: A Decision-Focused Learning Approach using Neural Networks
by: Islam, MD Shafikul, et al.
Published: (2023)
by: Islam, MD Shafikul, et al.
Published: (2023)
Neural Solver Selection for Combinatorial Optimization
by: Gao, Chengrui, et al.
Published: (2024)
by: Gao, Chengrui, et al.
Published: (2024)
Drug Release Modeling using Physics-Informed Neural Networks
by: Qureshi, Daanish Aleem, et al.
Published: (2026)
by: Qureshi, Daanish Aleem, et al.
Published: (2026)
Input Convex Kolmogorov Arnold Networks
by: Deschatre, Thomas, et al.
Published: (2025)
by: Deschatre, Thomas, et al.
Published: (2025)
SPOT: Spatio-Temporal Pattern Mining and Optimization for Load Consolidation in Freight Transportation Networks
by: Cheng, Sikai, et al.
Published: (2025)
by: Cheng, Sikai, et al.
Published: (2025)
Neur2BiLO: Neural Bilevel Optimization
by: Dumouchelle, Justin, et al.
Published: (2024)
by: Dumouchelle, Justin, et al.
Published: (2024)
Neural Combinatorial Optimization for Stochastic Flexible Job Shop Scheduling Problems
by: Smit, Igor G., et al.
Published: (2024)
by: Smit, Igor G., et al.
Published: (2024)
Training Safe Neural Networks with Global SDP Bounds
by: Soletskyi, Roman, et al.
Published: (2024)
by: Soletskyi, Roman, et al.
Published: (2024)
Applications of 0-1 Neural Networks in Prescription and Prediction
by: Patil, Vrishabh, et al.
Published: (2024)
by: Patil, Vrishabh, et al.
Published: (2024)
Taming Binarized Neural Networks and Mixed-Integer Programs
by: Aspman, Johannes, et al.
Published: (2023)
by: Aspman, Johannes, et al.
Published: (2023)
Unsupervised Training of Diffusion Models for Feasible Solution Generation in Neural Combinatorial Optimization
by: Hong, Seong-Hyun, et al.
Published: (2024)
by: Hong, Seong-Hyun, et al.
Published: (2024)
Adversarial Physics-Informed Machine Learning for Robust Optimal Safe Predefined-Time Stabilization: A Game-Theoretic Approach
by: Kokolakis, Nick-Marios T., et al.
Published: (2025)
by: Kokolakis, Nick-Marios T., et al.
Published: (2025)
Logistics Hub Location Optimization: A K-Means and P-Median Model Hybrid Approach Using Road Network Distances
by: Rahman, Muhammad Abdul, et al.
Published: (2023)
by: Rahman, Muhammad Abdul, et al.
Published: (2023)
Retrofitting Earth System Models with Cadence-Limited Neural Operator Updates
by: Bora, Aniruddha, et al.
Published: (2025)
by: Bora, Aniruddha, et al.
Published: (2025)
Feed-Forward Neural Networks as a Mixed-Integer Program
by: Aftabi, Navid, et al.
Published: (2024)
by: Aftabi, Navid, et al.
Published: (2024)
Graph Neural Networks for the Offline Nanosatellite Task Scheduling Problem
by: Pacheco, Bruno Machado, et al.
Published: (2023)
by: Pacheco, Bruno Machado, et al.
Published: (2023)
SMiLE: Provably Enforcing Global Relational Properties in Neural Networks
by: Francobaldi, Matteo, et al.
Published: (2025)
by: Francobaldi, Matteo, et al.
Published: (2025)
Ginger: An Efficient Curvature Approximation with Linear Complexity for General Neural Networks
by: Hao, Yongchang, et al.
Published: (2024)
by: Hao, Yongchang, et al.
Published: (2024)
KANtrol: A Physics-Informed Kolmogorov-Arnold Network Framework for Solving Multi-Dimensional and Fractional Optimal Control Problems
by: Aghaei, Alireza Afzal
Published: (2024)
by: Aghaei, Alireza Afzal
Published: (2024)
Crack Path Prediction with Operator Learning using Discrete Particle System data Generation
by: Kiyani, Elham, et al.
Published: (2025)
by: Kiyani, Elham, et al.
Published: (2025)
A Convexity-dependent Two-Phase Training Algorithm for Deep Neural Networks
by: Hrycej, Tomas, et al.
Published: (2025)
by: Hrycej, Tomas, et al.
Published: (2025)
An Improved Finite-time Analysis of Temporal Difference Learning with Deep Neural Networks
by: Ke, Zhifa, et al.
Published: (2024)
by: Ke, Zhifa, et al.
Published: (2024)
The Newton-Muon Optimizer
by: Du, Zhehang, et al.
Published: (2026)
by: Du, Zhehang, et al.
Published: (2026)
Similar Items
-
Curvature-Aware Optimization for High-Accuracy Physics-Informed Neural Networks
by: Jnini, Anas, et al.
Published: (2026) -
HJ-sampler: A Bayesian sampler for inverse problems of a stochastic process by leveraging Hamilton-Jacobi PDEs and score-based generative models
by: Meng, Tingwei, et al.
Published: (2024) -
On the Convergence of (Stochastic) Gradient Descent for Kolmogorov--Arnold Networks
by: Gao, Yihang, et al.
Published: (2024) -
Leveraging Hamilton-Jacobi PDEs with time-dependent Hamiltonians for continual scientific machine learning
by: Chen, Paula, et al.
Published: (2023) -
PINNs in PDE Constrained Optimal Control Problems: Direct vs Indirect Methods
by: Zhang, Zhen, et al.
Published: (2026)