Imagen de Portada

Shape Derivative-Informed Neural Operators with Application to Risk-Averse Shape Optimization

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Gong, Xindi, Luo, Dingcheng, O'Leary-Roseberry, Thomas, Nicholson, Ruanui, Ghattas, Omar
Formato:	Preprint
Publicado:	2026
Materias:	Optimization and Control Machine Learning Numerical Analysis
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Inference of Heterogeneous Material Properties via Infinite-Dimensional Integrated DIC
por: Kirchhoff, Joseph, et al.
Publicado: (2024)

Fast Unconstrained Optimization via Hessian Averaging and Adaptive Gradient Sampling Methods
por: O'Leary-Roseberry, Thomas, et al.
Publicado: (2024)

Derivative-Informed Fourier Neural Operator: Universal Approximation and Applications to PDE-Constrained Optimization
por: Yao, Boyuan, et al.
Publicado: (2025)

Dimension reduction for derivative-informed operator learning: An analysis of approximation errors
por: Luo, Dingcheng, et al.
Publicado: (2025)

Derivative-informed neural operator acceleration of geometric MCMC for infinite-dimensional Bayesian inverse problems
por: Cao, Lianghao, et al.
Publicado: (2024)

A Unified Phase-Field Fourier Neural Network Framework for Topology Optimization
por: Li, Jing, et al.
Publicado: (2025)

Distributionally Robust Shape and Topology Optimization
por: Dapogny, Charles, et al.
Publicado: (2025)

A note on the relationship between PDE-based precision operators and Matérn covariances
por: Villa, Umberto, et al.
Publicado: (2024)

A Shape Optimization Pipeline for Marine Propellers by means of Reduced Order Modeling Techniques
por: Ivagnes, Anna, et al.
Publicado: (2023)

Shape optimization of harmonic helicity in toroidal domains
por: Robin, Remi, et al.
Publicado: (2024)

LazyDINO: Fast, scalable, and efficiently amortized Bayesian inversion via structure-exploiting and surrogate-driven measure transport
por: Cao, Lianghao, et al.
Publicado: (2024)

Numerical Methods for Shape Optimal Design of Fluid-Structure Interaction Problems
por: Haubner, Johannes, et al.
Publicado: (2024)

A Generalized Primal-Dual Correction Method for Saddle-Point Problems with a Nonlinear Coupling Operator
por: Wang, Sai, et al.
Publicado: (2023)

Performance of Neural and Polynomial Operator Surrogates
por: Westermann, Josephine, et al.
Publicado: (2026)

Optimization methods for solving matrix equations
por: Zhang, Juan, et al.
Publicado: (2024)

Interpolation-Based Gradient-Error Bounds for Use in Derivative-Free Optimization of Noisy Functions
por: Marchetti, Alejandro G., et al.
Publicado: (2025)

Taylor Approximation Variance Reduction for Approximation Errors in PDE-constrained Bayesian Inverse Problems
por: Nicholson, Ruanui, et al.
Publicado: (2025)

Riemannian Optimization and the Hartree-Fock Method
por: da Silva, Caio O.
Publicado: (2024)

Analysis of Robin-boundary control for the Boussinesq equations
por: Gong, Wei, et al.
Publicado: (2026)

Risk reversal for least squares estimators under nested convex constraints
por: Al-Ghattas, Omar
Publicado: (2026)

A Priori Error Estimation of Physics-Informed Neural Networks Solving Allen--Cahn and Cahn--Hilliard Equations
por: Zhang, Guangtao, et al.
Publicado: (2024)

An Inexact General Descent Method with Applications in Differential Equation-Constrained Optimization
por: Macedo, Humberto Gimenes, et al.
Publicado: (2025)

On the Width Scaling of Neural Optimizers Under Matrix Operator Norms I: Row/Column Normalization and Hyperparameter Transfer
por: Xu, Ruihan, et al.
Publicado: (2026)

Global Convergence and Error Propagation in Neural Gradient Flows: A Riemannian Optimization Framework
por: Zheng, Shixin, et al.
Publicado: (2026)

Mixed-Derivative Total Variation
por: Guillemet, Vincent, et al.
Publicado: (2025)

Optimal Stirring Strategies for Passive Scalars in a Domain with a General Shape and No-Flux Boundary Condition
por: Zhu, Sirui, et al.
Publicado: (2024)

Risk-Averse Ensemble Control for Control-Affine Systems
por: Scagliotti, Alessandro, et al.
Publicado: (2026)

Scalable Acceleration for Classification-Based Derivative-Free Optimization
por: Han, Tianyi, et al.
Publicado: (2023)

Curvature-Aware Optimization for High-Accuracy Physics-Informed Neural Networks
por: Jnini, Anas, et al.
Publicado: (2026)

Bioluminescence tomography via a shape optimization method based on a complex-valued model
por: Wu, Qianqian, et al.
Publicado: (2025)

A Penalty Method for Non-Self-Adjoint Topology Optimization
por: Gong, Wei, et al.
Publicado: (2026)

A Simple Introduction to the SiMPL Method for Density-Based Topology Optimization
por: Kim, Dohyun, et al.
Publicado: (2024)

A novel shape optimization approach for source identification in elliptic equations
por: Gong, Wei, et al.
Publicado: (2024)

On the Approximation of Operator-Valued Riccati Equations in Hilbert Spaces
por: Cheung, James
Publicado: (2023)

Cost Attribution And Risk-Averse Unit Commitment In Power Grids Using Integrated Gradient
por: Carmona, Rene, et al.
Publicado: (2024)

BC-ADMM: An Efficient Non-convex Constrained Optimizer with Robotic Applications
por: Pan, Zherong, et al.
Publicado: (2025)

On Global Rates for Regularization Methods based on Secant Derivative Approximations
por: Cartis, Coralia, et al.
Publicado: (2025)

A Derivative-Free Saddle-search Algorithm With Linear Convergence Rate
por: Du, Qiang, et al.
Publicado: (2026)

Derivative-free optimization is competitive for aerodynamic design optimization in moderate dimensions
por: Plaban, Punya, et al.
Publicado: (2025)

Basic Inequalities for First-Order Optimization with Applications to Statistical Risk Analysis
por: Paik, Seunghoon, et al.
Publicado: (2025)