arXiv:2505.02974v3 Announce Type: replace Abstract: Machine learning-based surrogate models have emerged as a powerful tool to accelerate simulation-driven scientific workflows, but their adoption is limited by the lack of large-scale, diverse, and standardized datasets for physics-based simulations. Existing benchmarks often focus on narrow domains or rely on simplified data models, and fail to capture the heterogeneity arising from variable geometries, meshes, and topologies, which is critical for assessing generalization in realistic settings. We introduce PLAID (Physics-Learning AI Data mo
The increasing complexity of physics simulations and the demand for more robust and generalizable AI models necessitate unified data approaches to accelerate scientific discovery and engineering.
A unified data model for physics simulations addresses a critical bottleneck in deploying AI-based surrogate models, enabling wider adoption and accelerating simulation-driven scientific workflows across multiple sectors.
The introduction of a standardized and heterogeneous data model will allow machine learning models to generalize across diverse physics simulations, improving their utility and reducing the need for specialized benchmarks.
- · AI/ML researchers in scientific computing
- · Engineering and R&D sectors
- · Cloud providers offering ML platforms
- · Scientific software developers
- · Developers of highly specialized, non-standardized physics simulation datasets
Machine learning models will become more effective and widely applicable in accelerating physics-based simulations, reducing computational costs and development cycles.
This acceleration could lead to faster innovation in fields reliant on complex simulations, such as material science, aerospace, and climate modeling.
It may democratize access to advanced simulation capabilities through off-the-shelf AI models, potentially increasing the pace of scientific discovery and technological development globally.
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Read at arXiv cs.LG