arXiv Machine Learning

A Bayesian latent Gaussian process framework for aerodynamic uncertainty quantification

arXiv:2606. 28871v1 Announce Type: cross Abstract: Predicting the aerodynamic performance (e.

arXiv Machine Learning

Jul 9

Optimization-Embedded Active Multi-Fidelity Surrogate Learning for Multi-Condition Airfoil Shape Optimization

arXiv:2603. 17057v2 Announce Type: replace-cross Abstract: Active multi-fidelity surrogate modeling is developed for multi-condition airfoil shape optimization to reduce high-fidelity CFD cost while retaining RANS-consistent aerodynamic metrics.

By Isaac Robledo, Alberto Vilari\~no, Arnau Mir\'o, Oriol Lehmkuhl, Carlos Sanmiguel Vila, Rodrigo Castellanos

arXiv Machine Learning

Jun 15

Multi-fidelity aerodynamic data fusion by autoencoder transfer learning

arXiv:2512. 13069v2 Announce Type: replace Abstract: Accurate aerodynamic prediction often relies on high-fidelity simulations; however, their prohibitive computational costs severely limit their applicability in data-driven modeling.

By Javier Nieto-Centenero, Esther Andr\'es, Rodrigo Castellanos

rag fine-tuning

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

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

Aug 7

A Unified Risk View of Uncertainty: Posterior Risk for Disentanglement and Evaluation Beyond Proxies

arXiv:2608. 05995v1 Announce Type: new Abstract: Reliable uncertainty estimates are critical in safety-sensitive applications, where understanding the sources of predictive uncertainty is essential.

By Frieder Wizgall, Georg Tirpitz, Moritz Seiler, Kerstin Ritter, B\'alint Mucs\'anyi

benchmarks safety

arXiv Machine Learning

Jun 26

Ribbon: Scalable Approximation and Robust Uncertainty Quantification

arXiv:2606. 27269v1 Announce Type: cross Abstract: Reliably quantifying predictive uncertainty is difficult for complex, high-dimensional, or misspecified models.

By Graham Gibson, John Tipton, Kellin Rumsey, Natalie Klein

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 Machine Learning

Jun 2

On the Uncertainty Quantification Ability of Tabular Foundation Models

arXiv:2606. 01427v1 Announce Type: cross Abstract: Foundation models (FMs) have achieved substantial success in generalizing across tasks without problemspecific training or fine-tuning.

By Tyler R. Johnson, Kian Ben-Jacob, Nima Negarandeh, Oriol Vendrell-Gallart, Ramin Bostanabad

fine-tuning

arXiv Machine Learning

Jul 7

CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk

arXiv:2507. 08150v4 Announce Type: replace-cross Abstract: Accurate uncertainty quantification is critical for reliable predictive modeling.

By Ilia Azizi, Juraj Bodik, Jakob Heiss, Bin Yu

arXiv Machine Learning

Jul 2

TRIE: An Evaluation Framework for Stochastic PDE Surrogates

arXiv:2607. 00196v1 Announce Type: new Abstract: Many scientific systems exhibit uncertainty from stochastic forcing, unresolved degrees of freedom, or imperfect observations, making reliable surrogate forecasting fundamentally distributional rather than pointwise.

By Bharat Srikishan, Javier E. Santos, Nikhil Muralidhar, Charles D. Young

arXiv Machine Learning

Jun 19

Quantifying Aleatoric Uncertainty of In-Context Learning for Robust Measure of LLM Prediction Confidence

arXiv:2606. 19353v1 Announce Type: cross Abstract: In-Context Learning (ICL) allows LLMs to adapt to new tasks from a few demonstrations, but its reliability remains a concern: predictions are highly sensitive to both prompt design and the model's ability to understand the context, obscuring whether failures arise from data properties or model limitations.

By Jinseok Chung, Minkyoung Song, Hyunji Jung, Namhoon Lee

llms benchmarks safety

arXiv Machine Learning

Jun 4

Variance-Gated Ensembles: An Epistemic-Aware Framework for Uncertainty Estimation

arXiv:2602. 08142v2 Announce Type: replace Abstract: Machine learning applications require fast and reliable per-sample uncertainty estimation.

By H. Martin Gillis, Isaac Xu, Thomas Trappenberg

benchmarks

Related stories