Title: SIMSHIFT: A Benchmark for Adapting Neural Surrogates to Distribution Shifts
Primary area: datasets and benchmarks
Keywords: PDE, Neural Operators, Domain Adaptation, Distribution Shift

Abstract: Neural surrogates for Partial Differential Equations (PDEs) often suffer significant performance degradation when evaluated on unseen problem configurations, such as new initial conditions or structural dimensions.
Meanwhile, Domain Adaptation (DA) techniques have been widely used in vision and language processing to generalize from limited information about unseen configurations.
In this work, we address this gap through two focused contributions.
First, we introduce SIMSHIFT, a novel benchmark dataset and evaluation suite composed of four industrial simulation tasks spanning diverse processes and physics: _hot rolling_, _sheet metal forming_, _electric motor design_ and _heatsink design_.
Second, we extend established DA methods to state-of-the-art neural surrogates and systematically evaluate them.
These approaches use parametric descriptions and ground truth simulations from multiple source configurations, together with only parametric descriptions from target configurations.
The goal is to accurately predict target simulations without access to ground truth simulation data.
Extensive experiments on SIMSHIFT highlight the challenges of out of distribution neural surrogate modeling, demonstrate the potential of DA in simulation, and reveal open problems in achieving robust neural surrogates under distribution shifts in industrially relevant scenarios.

TL;DR: We introduce SIMSHIFT, a benchmark and dataset for domain adaptation of neural surrogates across four industrial simulation tasks with predefined distribution shifts.