arXiv Machine Learning August 5, 2026 By Edgar Jaber (CB, ENS Paris Saclay), R\'emy Vallot (CB, Michelin), Thibault Dairay (CB, Michelin), Mathilde Mougeot (CB, ENSIIE, ENS Paris Saclay)

Conformal risk control for model-form uncertainty in parametric non-intrusive reduced-order models

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arXiv:2608. 03360v1 Announce Type: cross Abstract: Non-intrusive reduced-order models (NIROMs) have become a standard tool for approximating parametric partial differential equations from computer design of experiments while significantly reducing computational costs.

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arXiv Machine Learning

Jun 9

Operator learning for the 2D incompressible Navier-Stokes equations: a conformal prediction approach in the data-scarce regime

arXiv:2606. 08654v1 Announce Type: new Abstract: In this paper, we propose a perturbation-based conformal prediction framework for uncertainty quantification in operator learning, with a focus on the 2D Navier--Stokes equations.

By Weinan Wang, Bowen Gang, Hao Deng

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arXiv Statistics ML

6d ago

Multivariate conformal uncertainty propagation in multitask atomistic simulation: Successes and pitfalls

The paper explores multivariate conformal uncertainty propagation for multitask atomistic simulations, introducing methods such as Bonferroni‑corrected hyperrectangles, hyperellipsoidal sets based on Mahalanobis distance, and custom loss functions within conformal risk control. It applies these techniques to calibrate predictions of energies, forces, and stresses, then propagates the resulting uncertainty sets to downstream quantities like elastic constants and vacancy formation energies. The study emphasizes how incorporating correlation predictions can capture symmetries and error cancellation, and discusses the interaction between computational protocols and conformal guarantees.

By Katharine Fisher, Michael Herbst, James Kermode, Youssef Marzouk

arXiv Machine Learning

Jul 21

Isotonic Conformal Prediction

arXiv:2607. 16675v1 Announce Type: cross Abstract: A point prediction that is well calibrated on average can still be systematically biased conditional on its own value, undermining its use in downstream decision-making.

By Daniel Bensimon, Sean Xiang Yu, Eric D. Kolaczyk, Archer Y. Yang

arXiv AI

Jun 10

Conformal Prediction for Neural Operators: Distribution-Free Uncertainty Quantification in Physics Simulation

arXiv:2606. 09923v1 Announce Type: cross Abstract: Neural operators such as the Fourier Neural Operator (FNO) have emerged as powerful surrogates for solving partial differential equations (PDEs), achieving speedups of several orders of magnitude over traditional numerical solvers.

By Michael Chin

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arXiv Machine Learning

Jul 1

Accelerating Conformal Prediction via Approximate Leave-One-Out

arXiv:2606. 31915v1 Announce Type: cross Abstract: While conformal prediction provides a general framework for uncertainty quantification in predictive inference, its application is often limited by computational cost.

By Jiachen Cong, Jingbo Liu

arXiv Machine Learning

Jun 11

Structure-Preserving Neural Surrogates with Tractable Uncertainty Quantification

arXiv:2606. 11650v1 Announce Type: new Abstract: Recent advances in scientific machine learning provide a means of near-real-time solution to partial differential equations (PDEs), but lack the theoretical underpinnings of conventional simulators that support contemporary verification and validation.

By Handi Zhang, Adrienne M. Propp, Brooks Kinch, Houman Owhadi, Nathaniel Trask

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Operator learning for the 2D incompressible Navier-Stokes equations: a conformal prediction approach in the data-scarce regime

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