arXiv AI

Developing Distance-Aware Physics-Constrained Probabilistic Frameworks for Industrial Prognostics

arXiv:2512. 08499v3 Announce Type: replace-cross Abstract: Development of reliable and physically interpretable probabilistic frameworks for industrial prognostics remain nascent, and existing literature is often insensitive as inputs move away from the training manifold.

arXiv AI

Jun 2

Physically-Constrained Mamba-SDE for Remaining Useful Life Prediction under Irregular Observations

arXiv:2606. 01894v1 Announce Type: new Abstract: Accurate Remaining Useful Life prediction is critical for industrial predictive maintenance.

By Deyu Zhuang, Peiliang Gong, Yang Shao, Liyuan Shu, Qi Zhu, Xiaoli Li, Daoqiang Zhang

rag benchmarks safety

arXiv Machine Learning

Jun 8

ScatterPrism: convergence for generative simulation and inverse problems in particle and nuclear physics

arXiv:2604. 01313v2 Announce Type: replace Abstract: High-fidelity simulations and complex inverse problems, such as detector modeling and unfolding, are computationally intensive bottlenecks across subatomic physics, yet essential for accurate physical interpretation.

By Zeyu Xia, Tyler Kim, Trevor Reed, Judy Fox, Geoffrey Fox, Adam Szczepaniak

diffusion benchmarks

arXiv AI

Jun 17

Geometry-Aware Post-Hoc Uncertainty Quantification in Operator Learning

arXiv:2606. 17513v1 Announce Type: cross Abstract: Neural operators provide fast surrogates for PDEs but their deterministic predictions limit their use in tasks requiring uncertainty quantification (UQ), especially under geometric variability.

By Oriol Vendrell-Gallart, Nima Negarandeh, Ramin Bostanabad

rag benchmarks

arXiv Machine Learning

Jun 5

Gradient-Flow Optimization as Dynamic Random-Effects Inference: Testing and Early Stopping with Applications to Deep Learning

arXiv:2605. 27991v2 Announce Type: replace-cross Abstract: Gradient-flow optimization is usually viewed as an algorithmic procedure for minimizing empirical loss, with training duration selected by validation or heuristic early-stopping rules.

By Minhao Yao, Ruoyu Wang, Xihong Lin, Lin Liu, Zhonghua Liu

arXiv Machine Learning

Jul 21

Representation-Aware Distributionally Robust Optimization: A Knowledge Transfer Framework

arXiv:2509. 09371v2 Announce Type: replace-cross Abstract: Distributionally robust optimization (DRO) protects statistical learning against distributional shifts by optimizing the worst-case performance over a set of perturbed distributions.

By Zitao Wang, Nian Si, Molei Liu

fine-tuning

arXiv AI

Jun 2

Toward accurate RUL and SoH estimation using reinforced graph-based physics-informed neural networks enhanced with dynamic weights

arXiv:2507. 09766v2 Announce Type: replace-cross Abstract: Accurate estimation of Remaining Useful Life (RUL) and State of Health (SoH) is essential for reliable Prognostics and Health Management (PHM), supporting timely maintenance and dependable industrial operation.

By Mohamadreza Akbari Pour, Ali Ghasemzadeh, Mohamad Ali Bijarchi, Mohammad Behshad Shafii

reinforcement-learning benchmarks

arXiv AI

Jul 14

Knowledge-Constrained Shape Optimization with a Mixture-of-Experts Neural Operator for High-Confidence Design

arXiv:2607. 09763v1 Announce Type: cross Abstract: Engineering shape optimization faces challenges in both expert-dependent problem setup and surrogate-model reliability.

By Wenhao Fan, Yuanwei Bin, Jianghan Gu, Wenfa Luo, Jiao Xiang, Yuntian Chen, Shiyi Chen

arXiv AI

Jun 11

Physics-Distilled Neural Network enabled by Large Language Models for Manufacturing Process-Property Predictive Modeling

arXiv:2606. 11605v1 Announce Type: cross Abstract: Predicting process-property relationships in manufacturing is often challenged by high experimental costs and the limited interpretability of complex 'black-box' models.

By Ge Song, Kiarash Naghavi Khanghah, Anandkumar Patel, Rajiv Malhotra, Hongyi Xu

llms efficiency safety

arXiv Machine Learning

2d ago

On the Brittleness of Maximum Likelihood Estimation for Gaussian Process Hyperparameter Optimization

arXiv:2608. 13793v1 Announce Type: cross Abstract: Machine learning (ML) has become an indispensable part of modern engineering design workflows.

By Tyler R. Johnson, Kian Ben-Jacob, Christopher P. Muller, Ramin Bostanabad

arXiv Machine Learning

Jul 22

Uncertainty Quantification for AI-Driven Crash Simulation Surrogates: A Comparative Study of Monte Carlo Dropout and Deep Ensemble on Open-Source Bumper Beam Benchmark

arXiv:2607. 18294v1 Announce Type: new Abstract: Machine learning surrogate models are increasingly being explored in engineering product development to augment simulation-driven design, offering near-instantaneous predictions that complement computationally expensive high-fidelity analyses.

By Sudeep Chavare

benchmarks safety

arXiv Machine Learning

Jun 25

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data

arXiv:2603. 22050v2 Announce Type: replace-cross Abstract: Supervised machine learning describes the practice of fitting a parameterized model to labeled input-output data.

By Atticus Rex, Elizabeth Qian, David Peterson

arXiv Machine Learning

6d ago

Variational Parameter Calibration with Physics-Aware Latent-Space Surrogates

arXiv:2608. 11435v1 Announce Type: new Abstract: Forward and inverse modeling of parametric dynamical systems requires surrogate models that are not only accurate for state prediction, but also informative for parameter calibration.

By Qiyao Zhou, Xujia Zhu, Pierre Joli, Yu Cong, Sibo Cheng

benchmarks

Related stories