The Scalable Scientific Machine Learning Lab designs scientific machine learning (SciML) algorithms and applies them to impactful problems across science.
We develop SciML techniques such as physics-informed neural networks, hybrid ML-numerical algorithms, and physics-based computer vision, and use them to accelerate simulations, better extract knowledge from data, discover new physical models, and improve experiment design.
We focus on designing algorithms which are 1) robust, designing physically-grounded workflows that generalise well, and 2) scalable, building algorithms that inherently handle multi-scale, high-dimensional, noisy, and realistic systems.
We are a highly cross-disciplinary team: our members are experts across machine learning, applied mathematics, high-performance computing, and in domain-specific areas including geophysics, climate science, and planetary science.
See our lab website to learn more about us.
