Accelerated Gradient-based Design Optimization Via Differentiable Physics-Informed Neural Operator: A Composites Autoclave Processing Case Study
Fuente:
arXiv
Guardado en:
| Autores principales: | Patel, Janak M., Ramezankhani, Milad, Deodhar, Anirudh, Birru, Dagnachew |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
GITO: Graph-Informed Transformer Operator for Learning Complex Partial Differential Equations
por: Ramezankhani, Milad, et al.
Publicado: (2025)
por: Ramezankhani, Milad, et al.
Publicado: (2025)
An Advanced Physics-Informed Neural Operator for Comprehensive Design Optimization of Highly-Nonlinear Systems: An Aerospace Composites Processing Case Study
por: Ramezankhani, Milad, et al.
Publicado: (2024)
por: Ramezankhani, Milad, et al.
Publicado: (2024)
FB-HyDON: Parameter-Efficient Physics-Informed Operator Learning of Complex PDEs via Hypernetwork and Finite Basis Domain Decomposition
por: Ramezankhani, Milad, et al.
Publicado: (2024)
por: Ramezankhani, Milad, et al.
Publicado: (2024)
State of Health Estimation of Batteries Using a Time-Informed Dynamic Sequence-Inverted Transformer
por: Patel, Janak M., et al.
Publicado: (2025)
por: Patel, Janak M., et al.
Publicado: (2025)
A Multi-Objective Genetic Algorithm for Healthcare Workforce Scheduling
por: Patel, Vipul, et al.
Publicado: (2025)
por: Patel, Vipul, et al.
Publicado: (2025)
DETNO: A Diffusion-Enhanced Transformer Neural Operator for Long-Term Traffic Forecasting
por: Ahmad, Owais, et al.
Publicado: (2025)
por: Ahmad, Owais, et al.
Publicado: (2025)
Numerical simulation of transient heat conduction with moving heat source using Physics Informed Neural Networks
por: Kalyan, Anirudh, et al.
Publicado: (2025)
por: Kalyan, Anirudh, et al.
Publicado: (2025)
Causal Operator Discovery in Partial Differential Equations via Counterfactual Physics-Informed Neural Networks
por: Katende, Ronald
Publicado: (2025)
por: Katende, Ronald
Publicado: (2025)
Physics-Informed Geometry-Aware Neural Operator
por: Zhong, Weiheng, et al.
Publicado: (2024)
por: Zhong, Weiheng, et al.
Publicado: (2024)
Accelerating Conjugate Gradient Solvers for Homogenization Problems with Unitary Neural Operators
por: Herb, Julius, et al.
Publicado: (2025)
por: Herb, Julius, et al.
Publicado: (2025)
Accelerating Conjugate Gradient Solvers for Homogenization Problems With Unitary Neural Operators
por: Julius Herb, et al.
Publicado: (2026)
por: Julius Herb, et al.
Publicado: (2026)
Surrogate-Guided Quantum Discovery in Black-Box Landscapes with Latent-Quadratic Interaction Embedding Transformers
por: Gopalakrishnan, Saisubramaniam, et al.
Publicado: (2026)
por: Gopalakrishnan, Saisubramaniam, et al.
Publicado: (2026)
Number Theoretic Accelerated Learning of Physics-Informed Neural Networks
por: Matsubara, Takashi, et al.
Publicado: (2023)
por: Matsubara, Takashi, et al.
Publicado: (2023)
An Overview on Machine Learning Methods for Partial Differential Equations: from Physics Informed Neural Networks to Deep Operator Learning
por: Gonon, Lukas, et al.
Publicado: (2024)
por: Gonon, Lukas, et al.
Publicado: (2024)
Newton Informed Neural Operator for Computing Multiple Solutions of Nonlinear Partials Differential Equations
por: Hao, Wenrui, et al.
Publicado: (2024)
por: Hao, Wenrui, et al.
Publicado: (2024)
ORPO-Distill: Mixed-Policy Preference Optimization for Cross-Architecture LLM Distillation
por: Singh, Aasheesh, et al.
Publicado: (2025)
por: Singh, Aasheesh, et al.
Publicado: (2025)
HI-SQL: Optimizing Text-to-SQL Systems through Dynamic Hint Integration
por: Parab, Ganesh, et al.
Publicado: (2025)
por: Parab, Ganesh, et al.
Publicado: (2025)
Optimizing Variational Physics-Informed Neural Networks Using Least Squares
por: Uriarte, Carlos, et al.
Publicado: (2024)
por: Uriarte, Carlos, et al.
Publicado: (2024)
Ensemble learning for Physics Informed Neural Networks: a Gradient Boosting approach
por: Fang, Zhiwei, et al.
Publicado: (2023)
por: Fang, Zhiwei, et al.
Publicado: (2023)
PhysicsSolver: Transformer-Enhanced Physics-Informed Neural Networks for Forward and Forecasting Problems in Partial Differential Equations
por: Zhu, Zhenyi, et al.
Publicado: (2025)
por: Zhu, Zhenyi, et al.
Publicado: (2025)
Preconditioning for Accelerated Gradient Descent Optimization and Regularization
por: Ye, Qiang
Publicado: (2024)
por: Ye, Qiang
Publicado: (2024)
Incorporating Continuous Dependence Qualifies Physics-Informed Neural Networks for Operator Learning
por: Li, Guojie, et al.
Publicado: (2026)
por: Li, Guojie, et al.
Publicado: (2026)
Convergence of Stochastic Gradient Methods for Wide Two-Layer Physics-Informed Neural Networks
por: Jin, Bangti, et al.
Publicado: (2025)
por: Jin, Bangti, et al.
Publicado: (2025)
A Unified Benchmark of Physics-Informed Neural Networks and Kolmogorov-Arnold Networks for Ordinary and Partial Differential Equations
por: Dzimah, Salvador K., et al.
Publicado: (2026)
por: Dzimah, Salvador K., et al.
Publicado: (2026)
WINO: A Weak-Form Physics Informed Neural Operator for Hyperelasticity on Variable Domains
por: Zhu, Bokai, et al.
Publicado: (2026)
por: Zhu, Bokai, et al.
Publicado: (2026)
Parameter Inference based on Gaussian Processes Informed by Nonlinear Partial Differential Equations
por: Li, Zhaohui, et al.
Publicado: (2022)
por: Li, Zhaohui, et al.
Publicado: (2022)
Locally Subspace-Informed Neural Operators for Efficient Multiscale PDE Solving
por: Rudikov, Alexander, et al.
Publicado: (2025)
por: Rudikov, Alexander, et al.
Publicado: (2025)
Shape Derivative-Informed Neural Operators with Application to Risk-Averse Shape Optimization
por: Gong, Xindi, et al.
Publicado: (2026)
por: Gong, Xindi, et al.
Publicado: (2026)
Modelling parametric uncertainty in PDEs models via Physics-Informed Neural Networks
por: Panahi, Milad, et al.
Publicado: (2024)
por: Panahi, Milad, et al.
Publicado: (2024)
Pretrain Finite Element Method: A Pretraining and Warm-start Framework for PDEs via Physics-Informed Neural Operators
por: Wang, Yizheng, et al.
Publicado: (2026)
por: Wang, Yizheng, et al.
Publicado: (2026)
Data-Guided Physics-Informed Neural Networks for Solving Inverse Problems in Partial Differential Equations
por: Zhou, Wei, et al.
Publicado: (2024)
por: Zhou, Wei, et al.
Publicado: (2024)
On the Stability and Convergence of Physics Informed Neural Networks
por: Gazoulis, Dimitrios, et al.
Publicado: (2023)
por: Gazoulis, Dimitrios, et al.
Publicado: (2023)
DMIS: Dynamic Mesh-based Importance Sampling for Training Physics-Informed Neural Networks
por: Yang, Zijiang, et al.
Publicado: (2022)
por: Yang, Zijiang, et al.
Publicado: (2022)
Mapping-based Hard-constrained Physics-Informed Neural Networks for unbounded wave problems
por: Zhang, Tao, et al.
Publicado: (2026)
por: Zhang, Tao, et al.
Publicado: (2026)
Dual Cone Gradient Descent for Training Physics-Informed Neural Networks
por: Hwang, Youngsik, et al.
Publicado: (2024)
por: Hwang, Youngsik, et al.
Publicado: (2024)
Design-Informed Generative Modelling using Structural Optimization
por: Sarma, Lowhikan Sivanantha, et al.
Publicado: (2023)
por: Sarma, Lowhikan Sivanantha, et al.
Publicado: (2023)
HAMLET: Graph Transformer Neural Operator for Partial Differential Equations
por: Bryutkin, Andrey, et al.
Publicado: (2024)
por: Bryutkin, Andrey, et al.
Publicado: (2024)
Component Fourier Neural Operator for Singularly Perturbed Differential Equations
por: Li, Ye, et al.
Publicado: (2024)
por: Li, Ye, et al.
Publicado: (2024)
Neural Operators with Localized Integral and Differential Kernels
por: Liu-Schiaffini, Miguel, et al.
Publicado: (2024)
por: Liu-Schiaffini, Miguel, et al.
Publicado: (2024)
Coupling Physics Informed Neural Networks with External Solvers
por: Halder, Rahul, et al.
Publicado: (2025)
por: Halder, Rahul, et al.
Publicado: (2025)
Ejemplares similares
-
GITO: Graph-Informed Transformer Operator for Learning Complex Partial Differential Equations
por: Ramezankhani, Milad, et al.
Publicado: (2025) -
An Advanced Physics-Informed Neural Operator for Comprehensive Design Optimization of Highly-Nonlinear Systems: An Aerospace Composites Processing Case Study
por: Ramezankhani, Milad, et al.
Publicado: (2024) -
FB-HyDON: Parameter-Efficient Physics-Informed Operator Learning of Complex PDEs via Hypernetwork and Finite Basis Domain Decomposition
por: Ramezankhani, Milad, et al.
Publicado: (2024) -
State of Health Estimation of Batteries Using a Time-Informed Dynamic Sequence-Inverted Transformer
por: Patel, Janak M., et al.
Publicado: (2025) -
A Multi-Objective Genetic Algorithm for Healthcare Workforce Scheduling
por: Patel, Vipul, et al.
Publicado: (2025)