Computational Astrophysics: Machine Learning X Astrophysics

About

Machine learning is being increasingly integrated into scientific discovery to augment and accelerate research.

Our researchers use ML to generate hypotheses, design experiments, collect and interpret large datasets, and gain insights that might not have been possible using traditional scientific methods alone.

We are at the forefront of this revolution. We led the first application of 3D convolutional neural network in astrophysics, the first application of machine learning to accelerate cosmological simulations, first development of deep learning accelerated simulation based inference for large scale spectroscopic surveys and first combined symbolic regression with neural network to extract scientific insights. More recently, our researchers and collaborators are leading the development of Foundation Model for Sciences and are excited to discover what large pre-trained models can do for science in general.

Polymathic AI Research

The Polymathic AI initiative researches a new class of foundation models for scientific data, developing models that share scientific concepts across disciplines. We aim to accelerate the development of versatile foundation models tailored for machine learning tasks in science. A breakthrough in this field would mark a significant shift analogous to the one seen in vision and natural language processing (NLP).

Our team comprises both pure machine learning researchers and domain scientists, covering a range of disciplines. A scientific advisory group of world-leading experts guides the initiative.

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