LLVM powers the core development tools, operating systems, and most applications at Apple Computer, where it long ago ...

Physics-aware machine learning integrates domain-specific physical knowledge into machine learning models, leading to the development of physics-informed neural networks (PINNs). PINNs embed physical ...

To get started with XLB, you can install it using pip. There are different installation options depending on your hardware and needs: The changelog for the releases can be found here. For examples to ...

“Just when I thought I was out, they pull me back in!” With a sly grin that I’d soon come to recognize, Paul Ginsparg quoted Michael Corleone from The Godfather. Ginsparg, a physics professor at ...

A distinguishing feature of the neural network models used in Physics and Chemistry is that they must obey basic underlying symmetries, such as symmetry to translations, rotations, and the exchange of ...

Understanding the physical dynamics of probe diffusion is critical for uncovering subtle interactions between particles and their environments, offering insights into evolving transport mechanisms at ...

Department of Computing & UKRI Centre for Doctoral Training in AI for Healthcare, Imperial College London, London SW7 2AZ, United Kingdom Department of Materials, Department of Bioengineering & ...

Over the past few months, our Machine Learning Foundations team at Microsoft Research has released a suite of small language models (SLMs) called “Phi” that achieve remarkable performance on a variety ...

The second-order analysis of slender steel members can be challenging, particularly when large deflections are involved. This research introduces a novel Machine Learning-based Structural Analysis ...

We share the story of how we made this paper, the first executable paper in Heliophysics, through cross-disciplinary collaboration to highlight the benefits of our process. Executable papers are ...