In this talk, we will introduce a state-of-the-art scientific machine learning paradigm - differentiable physics (DiffPhys ... Bio: Romit Maulik is an Assistant Professor of Data Science in the ...

In business, AI and machine learning ... the evolution of data-driven decision making, as it can become a critical tool for making informed decisions based on real-time information rather than ...

Physics-informed model creates reconstructions based on experimental data, revealing plasma asymmetries and helping optimize ...

Machine learning is becoming an essential part of a physicist’s toolkit. How should new students learn to use it? When Radha Mastandrea started her undergraduate physics program ... to process data ...

In contrast, the author's machine learning algorithm is equipped with atmospheric physics equations that can produce more accurate results faster and with less data. Hurricanes, or tropical ...

Machine learning algorithms can rapidly analyze vast sums of data, uncovering patterns, insights and trends. This information can ... enabling you to take a data-driven, iterative approach to ...

Not only do artificial neural networks play a key role in generative AI tools, they are also vital for the kind of complex data analysis done in particle physics, astrophysics, and more.

Yet for decades scientists have been seeking to change that via machine-learning approaches that emulate the brain’s adaptive computational prowess. The 2024 Nobel Prize in Physics was awarded ...

A study published in Physical Review Letters outlines a new approach for extracting information ... define the best labels? Machine learning provides a powerful, data-driven solution." ...