arXiv Machine Learning July 30, 2026 By Andrea Serani, Giorgio Palma, Matteo Diez

A nonlinear extension of parametric model embedding for dimensionality reduction in parametric shape design

Read the original on arXiv Machine Learning →

arXiv:2605. 11759v2 Announce Type: replace-cross Abstract: Dimensionality reduction is essential in simulation-based shape design, where high-dimensional parameterizations hinder optimization, surrogate modeling, and systematic design-space exploration.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI

Jul 14

Incremental Transformer for Surrogate-Based Inverse Design of Geopolymer Mixtures

arXiv:2607. 10896v1 Announce Type: new Abstract: Small-data inverse design is challenging in engineering informatics when observations are heterogeneous, mixed-type, and constrained by physical relations among design variables.

By Giansalvo Cirrincione, Filippo Grassia

llms benchmarks

arXiv AI

23h ago

Adaptive surrogate modeling for high-dimensional spatio-temporal output

arXiv:2608. 17250v1 Announce Type: cross Abstract: This paper develops an adaptive surrogate modeling method for problems with very high-dimensional spatio-temporal outputs.

By Berkcan Kapusuzoglu, Shunsaku Matsumoto, Yoshitomo Miyagi, Daigo Watanabe, Sankaran Mahadevan

diffusion

arXiv Machine Learning

Jul 1

Sparse POD Mode Selection and Manifold Dimensionality Reduction with Neural Networks

arXiv:2605. 27756v2 Announce Type: replace-cross Abstract: Linear dimensionality reduction methods such as proper orthogonal decomposition (POD) make high-dimensional data amenable to analysis by identifying the principal components, or modes, that capture the most variance, or energy, in the data and constructing a low-dimensional representation in the subspace they span.

By Tomoki Koike, Prakash Mohan, Marc T. Henry de Frahan, Elizabeth Qian, Julie Bessac

efficiency benchmarks safety

arXiv AI

Jun 15

SMART: Scalable Mesh-free Aerodynamic Simulations from Raw Geometries using a Transformer-based Surrogate Model

arXiv:2601. 18707v2 Announce Type: replace-cross Abstract: Machine learning-based surrogate models have emerged as more efficient alternatives to numerical solvers for physical simulations over complex geometries, such as car bodies.

By Jan Hagnberger, Mathias Niepert

llms diffusion

arXiv Machine Learning

Aug 10

Understanding Differentiable Embeddings Through Differential and Integral Geometry

arXiv:2608. 06809v1 Announce Type: new Abstract: How can an analyst decide whether a nonlinear dimensionality reduction embedding can be trusted?

By Xinyu Zhang, Klaus Mueller

rag

arXiv Machine Learning

Jun 3

LAMP: Data-Efficient Linear Affine Weight-Space Models for Parameter-Controlled 3D Shape Generation and Extrapolation

arXiv:2510. 22491v3 Announce Type: replace Abstract: Generating high-fidelity 3D geometries under explicit parameter constraints is central to engineering design, yet current methods often require large datasets and fail to provide reliable control beyond the training distribution.

By Ghadi Nehme, Yanxia Zhang, Dule Shu, Matt Klenk, Faez Ahmed

safety

Related stories

Incremental Transformer for Surrogate-Based Inverse Design of Geopolymer Mixtures

Adaptive surrogate modeling for high-dimensional spatio-temporal output

Sparse POD Mode Selection and Manifold Dimensionality Reduction with Neural Networks

SMART: Scalable Mesh-free Aerodynamic Simulations from Raw Geometries using a Transformer-based Surrogate Model

Understanding Differentiable Embeddings Through Differential and Integral Geometry

LAMP: Data-Efficient Linear Affine Weight-Space Models for Parameter-Controlled 3D Shape Generation and Extrapolation