A new technical paper, “Protonic nickelate device networks for spatiotemporal neuromorphic computing,” was published by researcher at UCSD and Rutgers University. Abstract “Computation in biological ...

Researchers present a comprehensive review of frontier AI applications in computational structural analysis from 2020 to 2025 ...

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 ...

Abstract: This paper introduces a Physics-Informed Koopman Neural Operator (PI-KNO) for augmented dynamics visual servoing of multirotors that integrates Koopman operator theory with neural networks.

Abstract: Deep learning models trained on finite data lack a complete understanding of the physical world. On the other hand, physics-informed neural networks (PINNs) are infused with such knowledge ...

Explore the advancements in minimal residual disease (MRD) assays, comparing tumor-informed and tumor-agnostic methods for enhanced cancer detection and treatment strategies. Minimal residual disease ...

One of the key steps in developing new materials is property identification, which has long relied on massive amounts of experimental data and expensive equipment, limiting research efficiency. A ...

Doug Bonderud is an award-winning writer capable of bridging the gap between complex and conversational across technology, innovation and the human condition. As artificial intelligence becomes ...

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 ...

El Niño-Southern Oscillation (ENSO) is the strongest interannual variability signal in Earth's climate system. The shifts between its warm and cold phases profoundly impact global extreme weather, ...