🔥 The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning

💡 The paper introduces a large scale dataset collection called The Well which provides diverse numerical simulations for machine learning models in physical systems simulation. The problem addressed is that standard datasets in this space often cover small classes of physical behavior making it difficult to evaluate the efficacy of new approaches. To address this gap the authors created The Well which is a collection of datasets containing numerical simulations of a wide variety of spatiotemporal physical systems. The dataset draws from domain experts and numerical software developers and provides 15 terabytes of data across 16 datasets covering diverse domains such as biological systems fluid dynamics acoustic scattering and magneto hydrodynamic simulations. The authors also provide a unified PyTorch interface for training and evaluating models to facilitate usage of The Well. The dataset and code are available for use and the authors demonstrate the function of the library by introducing example baselines that highlight the new challenges posed by the complex dynamics of The Well. The main contribution of the paper is the creation of a large scale diverse dataset that can be used to benchmark machine learning models in physical systems simulation and provide a more comprehensive evaluation of their efficacy.

📅 Published on Nov 30, 2024

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2412.00568
• PDF: https://arxiv.org/pdf/2412.00568

📊 Datasets citing this paper:
• https://huggingface.co/datasets/polymathic-ai/acoustic_scattering_inclusions
• https://huggingface.co/datasets/polymathic-ai/rayleigh_benard
• https://huggingface.co/datasets/polymathic-ai/planetswe

🚀 Spaces citing this paper:
• https://huggingface.co/spaces/polymathic-ai/TheWell

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📢 By: https://xn--r1a.website/PaperNexus

#PhysicsSimulations #MachineLearningModels #PhysicalSystemsSimulation #NumericalSimulations #SpatiotemporalData