Physics-informed neural networks (PINNs) represent a burgeoning paradigm in computational science, whereby deep learning frameworks are augmented with explicit physical laws to solve both forward and ...

Why predictions fail: Traditional models struggle with threshold-driven landscapes like the Prairie Pothole Region, where water storage and sudden connectivity make streamflow unpredictable. AI’s new ...

From the outset, Alsym set a clear objective: to develop a truly non-flammable and high-performance battery chemistry. The physics-informed AI platform enabled this goal-guiding the discovery and ...

Rose Yu has a plan for how to make AI better, faster and smarter — and it’s already yielding results. When she was 10 years old, Rose Yu got a birthday present that would change her life — and, ...

From mapping deep mantle deformation to predicting seismic responses, AI is redefining how geoscientists tackle scarce and complex data. Semi-supervised learning, physics-informed models, and ...

If your jaw dropped as you watched the latest AI-generated video, your bank balance was saved from criminals by a fraud detection system, or your day was made a little easier because you were able to ...

The knowledge-informed deep learning (KIDL) paradigm, with the blue section representing the LLM workflow (teacher demonstration), the orange section representing the distillation pipeline of KIDL ...