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[2602.15820] Stabilizing Test-Time Adaptation of High-Dimensional Simulation Surrogates via D-Optimal Statistics
Summary
This article presents a framework for Test-Time Adaptation (TTA) of high-dimensional simulation surrogates using D-optimal statistics, addressing performance degradation due to distribution shifts during deployment.
Why It Matters
As machine learning surrogates become integral in engineering simulations, their reliability under varying conditions is crucial. This research introduces a novel TTA approach that enhances stability and performance in high-dimensional settings, potentially transforming practices in simulation and design optimization.
Key Takeaways
- Proposes a TTA framework that uses D-optimal statistics for better adaptation.
- Addresses performance issues in high-dimensional simulation surrogates.
- Demonstrates up to 7% improvement in out-of-distribution performance.
- Validates the method on established benchmarks like SIMSHIFT and EngiBench.
- Highlights the importance of stable adaptation in engineering applications.
Computer Science > Machine Learning arXiv:2602.15820 (cs) [Submitted on 17 Feb 2026] Title:Stabilizing Test-Time Adaptation of High-Dimensional Simulation Surrogates via D-Optimal Statistics Authors:Anna Zimmel, Paul Setinek, Gianluca Galletti, Johannes Brandstetter, Werner Zellinger View a PDF of the paper titled Stabilizing Test-Time Adaptation of High-Dimensional Simulation Surrogates via D-Optimal Statistics, by Anna Zimmel and 4 other authors View PDF Abstract:Machine learning surrogates are increasingly used in engineering to accelerate costly simulations, yet distribution shifts between training and deployment often cause severe performance degradation (e.g., unseen geometries or configurations). Test-Time Adaptation (TTA) can mitigate such shifts, but existing methods are largely developed for lower-dimensional classification with structured outputs and visually aligned input-output relationships, making them unstable for the high-dimensional, unstructured and regression problems common in simulation. We address this challenge by proposing a TTA framework based on storing maximally informative (D-optimal) statistics, which jointly enables stable adaptation and principled parameter selection at test time. When applied to pretrained simulation surrogates, our method yields up to 7% out-of-distribution improvements at negligible computational cost. To the best of our knowledge, this is the first systematic demonstration of effective TTA for high-dimensional simulation...