Cover Image

Neural topology optimization: the good, the bad, and the ugly

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Sanu, Suryanarayanan Manoj, Aragon, Alejandro M., Bessa, Miguel A.
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Numerical Analysis
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Gradient-free neural topology optimization: Towards effective fracture-resistant designs
by: Kus, Gawel, et al.
Published: (2024)

Consistent machine learning for topology optimization with microstructure-dependent neural network material models
by: Vijayakumaran, Harikrishnan, et al.
Published: (2024)

Automatically Differentiable Model Updating (ADiMU): conventional, hybrid, and neural network material model discovery including history-dependency
by: Ferreira, Bernardo P., et al.
Published: (2025)

Cooperative data-driven modeling
by: Dekhovich, Aleksandr, et al.
Published: (2022)

Quantile-Based Randomized Kaczmarz for Corrupted Tensor Linear Systems
by: Castillo, Alejandra, et al.
Published: (2025)

Generalization Bounds for Physics-Informed Neural Networks for the Incompressible Navier-Stokes Equations
by: Andre-Sloan, Sebastien, et al.
Published: (2026)

Solving Forward and Inverse Problems of Contact Mechanics using Physics-Informed Neural Networks
by: Sahin, T., et al.
Published: (2023)

Continuum Attention for Neural Operators
by: Calvello, Edoardo, et al.
Published: (2024)

The good, the bad and the ugly sides of data augmentation: An implicit spectral regularization perspective
by: Lin, Chi-Heng, et al.
Published: (2022)

Parallel-in-Time Solutions with Random Projection Neural Networks
by: Betcke, Marta M., et al.
Published: (2024)

Neural Conjugate Flows: Physics-informed architectures with flow structure
by: Bizzi, Arthur, et al.
Published: (2024)

Tensor train completion: local recovery guarantees via Riemannian optimization
by: Budzinskiy, Stanislav, et al.
Published: (2021)

E-PINNs: Epistemic Physics-Informed Neural Networks
by: Jacob, Bruno, et al.
Published: (2025)

A Microstructure-based Graph Neural Network for Accelerating Multiscale Simulations
by: Storm, J., et al.
Published: (2024)

On the optimal approximation of Sobolev and Besov functions using deep ReLU neural networks
by: Yang, Yunfei
Published: (2024)

An extended physics informed neural network for preliminary analysis of parametric optimal control problems
by: Demo, Nicola, et al.
Published: (2021)

Neural Low-Discrepancy Sequences
by: Van Huffel, Michael Etienne, et al.
Published: (2025)

Multigrade Neural Network Approximation
by: Zhang, Shijun, et al.
Published: (2026)

Beyond Regular Grids: Fourier-Based Neural Operators on Arbitrary Domains
by: Lingsch, Levi, et al.
Published: (2023)

Geometrical structures of digital fluctuations in parameter space of neural networks trained with adaptive momentum optimization
by: Netay, Igor V.
Published: (2024)

Data-Parallel Neural Network Training via Nonlinearly Preconditioned Trust-Region Method
by: Alegría, Samuel A. Cruz, et al.
Published: (2025)

Preconditioning for Physics-Informed Neural Networks
by: Liu, Songming, et al.
Published: (2024)

THINNs: Thermodynamically Informed Neural Networks
by: Castro, Javier, et al.
Published: (2025)

Solving Roughly Forced Nonlinear PDEs via Misspecified Kernel Methods and Neural Networks
by: Baptista, Ricardo, et al.
Published: (2025)

Message-Passing Monte Carlo: Generating low-discrepancy point sets via Graph Neural Networks
by: Rusch, T. Konstantin, et al.
Published: (2024)

Physics-Informed Geometry-Aware Neural Operator
by: Zhong, Weiheng, et al.
Published: (2024)

A Mathematical Analysis of Neural Operator Behaviors
by: Le, Vu-Anh, et al.
Published: (2024)

Graph Neural Regularizers for PDE Inverse Problems
by: Lauga, William, et al.
Published: (2025)

Neural Measures for learning distributions of Random PDEs
by: Arampatzis, Georgios, et al.
Published: (2025)

Dual-Balancing for Physics-Informed Neural Networks
by: Zhou, Chenhong, et al.
Published: (2025)

Estimating condition number with Graph Neural Networks
by: Carson, Erin, et al.
Published: (2026)

Supervised and Unsupervised Neural Network Solver for First Order Hyperbolic Nonlinear PDEs
by: Baba, Zakaria, et al.
Published: (2026)

Learning from Linear Algebra: A Graph Neural Network Approach to Preconditioner Design for Conjugate Gradient Solvers
by: Trifonov, Vladislav, et al.
Published: (2024)

Quantitative Approximation for Neural Operators in Nonlinear Parabolic Equations
by: Furuya, Takashi, et al.
Published: (2024)

Solving Poisson Equations using Neural Walk-on-Spheres
by: Nam, Hong Chul, et al.
Published: (2024)

Universal Approximation of Operators with Transformers and Neural Integral Operators
by: Zappala, Emanuele, et al.
Published: (2024)

Decentralized Neural Networks for Robust and Scalable Eigenvalue Computation
by: Katende, Ronald
Published: (2024)

MODNO: Multi Operator Learning With Distributed Neural Operators
by: Zhang, Zecheng
Published: (2024)

Multi-Level Monte Carlo Training of Neural Operators
by: Rowbottom, James, et al.
Published: (2025)

Revisiting Orbital Minimization Method for Neural Operator Decomposition
by: Ryu, J. Jon, et al.
Published: (2025)