PHASE: Physics-Integrated, Heterogeneity-Aware Surrogates for Scientific Simulations

• URL: http://arxiv.org/abs/2509.23453v1
• Date: Sat, 27 Sep 2025 18:50:40 GMT
• Title: PHASE: Physics-Integrated, Heterogeneity-Aware Surrogates for Scientific Simulations
• Authors: Dawei Gao, Dali Wang, Zhuowei Gu, Qinglei Cao, Xiao Wang, Peter Thornton, Dan Ricciuto, Yunhe Feng,
• Abstract summary: We introduce PHASE, a modular deep-learning framework for physics-integrated, heterogeneity-aware surrogates in scientific simulations.<n>Phase combines data-type-aware encoders for heterogeneous inputs with multi-level physics-based constraints that promote consistency from local dynamics to global system behavior.<n>We validate PHASE on the biogeochemical (BGC) spin-up workflow of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) Land Model.
• Score: 14.492366415635416
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large-scale numerical simulations underpin modern scientific discovery but remain constrained by prohibitive computational costs. AI surrogates offer acceleration, yet adoption in mission-critical settings is limited by concerns over physical plausibility, trustworthiness, and the fusion of heterogeneous data. We introduce PHASE, a modular deep-learning framework for physics-integrated, heterogeneity-aware surrogates in scientific simulations. PHASE combines data-type-aware encoders for heterogeneous inputs with multi-level physics-based constraints that promote consistency from local dynamics to global system behavior. We validate PHASE on the biogeochemical (BGC) spin-up workflow of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) Land Model (ELM), presenting-to our knowledge-the first scientifically validated AI-accelerated solution for this task. Using only the first 20 simulation years, PHASE infers a near-equilibrium state that otherwise requires more than 1,200 years of integration, yielding an effective reduction in required integration length by at least 60x. The framework is enabled by a pipeline for fusing heterogeneous scientific data and demonstrates strong generalization to higher spatial resolutions with minimal fine-tuning. These results indicate that PHASE captures governing physical regularities rather than surface correlations, enabling practical, physically consistent acceleration of land-surface modeling and other complex scientific workflows.

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