Our Scalable Scientific Machine Learning Lab @ Imperial College London website is now live! We build robust, scalable SciML methods to accelerate science — from de-noising Moon images to speeding up multi-scale simulation @ESEImperial @imperialcollege

I will be giving a mini-course on efficient physics-informed neural networks at the CWI Scientific Machine Learning and Numerical Methods Autumn School in October in the Netherlands - open to everyone, more info here:

Faster population balance equation (PBE) simulations with JAX!.PBEs model crystallization, chemical reactors & biological systems - but traditional solvers are too slow. Our new JAX solver accelerates computation & enables AI-driven physics discovery.

New paper: accurately modelling multiphase fluid flow in fractured porous media using physics-informed neural networks, validated using real CT-scan data. Work led by Jassem Abbasi and others - it was a pleasure to collaborate!

Having recently joined the department, I can say I have felt very supported and inspired starting my lab here, and it would be great to work alongside more experts in these areas! @ESEImperial.

RT @PatrickKidger: 💫Fellow scientific computing geeks, here is an example of adaptive mesh refinement in #JAX:. I….

🚀 Excited to share that I’m joining @ImperialCollege in Nov as a Lecturer, leading the Scalable SciML Lab in @ESEImperial! I’m also honoured to be a Schmidt AI in Science Fellow at @ImperialX_AI. I'm hiring PhD students! 🌍✨.

RT @DrJimFan: This may be the most important figure in LLM research since the OG Chinchilla scaling law in 2022. The key insight is 2 curve….

We just released our ETH Zürich AI in the Sciences and Engineering Master’s course on YouTube! 📚. Prof. Siddhartha Mishra and I explain PINNs, neural operators, foundation models, neural PDEs, diffusion models, symbolic regression, and more! #AI4Science.

Can we carry out multi-scale simulation with physics-informed neural networks (PINNs)? PINNs often struggle to solve multi-scale problems, and in our new paper we take steps to overcome this by combining them with multiple levels of domain decompositions:

RT @ETH_AI_Center: 🚀 Exciting news! Applications for ETH AI Center PhD and Post-Doc Fellowships are now OPEN! 🤩 . Apply by Nov 22, 2023, an….

Here is the recording:

I will be giving a guest lecture for the Physical Society of Zürich tonight 7.30pm on "How to incorporate physical understanding into machine learning" - admission is free and the talk will be recorded afterwards!

Just released a major new update to our FBPINNs library, which now uses JAX. Solve forward and inverse problems related to PDEs using PINNs + domain decomposition, and scale to 1000s+ subdomains! #jax #pinns #PDEs

Very excited to announce that our entire ETH Zürich Deep Learning in Scientific Computing Master's course is now on YouTube! 📖. Prof. Siddhartha Mishra and I will talk you through PINNs, neural operators, neural ODEs, differentiable physics and more

Interested in investigating how elephants use ground vibrations to communicate? + collecting data in Kenya? The Animal Vibration Lab at the @UniofOxford (a group I have collaborated with) has an open postdoctoral position!

Excited to announce that I am interning in NVIDIA's Modulus team. Modulus is a scientific machine learning framework which blends together neural networks with PDEs, allowing more robust models to be built for scientific analysis:

I will be presenting our latest research on finite basis physics-informed neural networks as a poster at The Symbiosis of Deep Learning and Differential Equations workshop at NeurIPS tomorrow: come along to find out more!

RT @CompSciOxford: Ben Moseley @benm_oseley, a DPhil student co-supervised by Andrew Markham @CompSciOxford and Tarje Nissen-Meyer @OxUniEa….