Free interactive tool uses particle physics-inspired Burkeanomics to predict prosperity impacts from policies in the ...

Abstract: A novel meshless electromagnetic (EM) simulation framework based on Physics-Informed Neural Networks (PINNs), enhanced by the integration of Kolmogorov–Arnold Networks (KANs) is presented.

Overview:  Explore the leading Physical AI development platforms used for robot simulation, reinforcement learning, synthetic ...

In a significant step towards strengthening India's future defence and security talent pool, Thakur College of Science and ...

Disclaimer: This column is merely a guiding voice and provides advice and suggestions on education and careers. The writer is ...

Embodied AI world models drew $6 billion in Q1 2026 alone, but new analysis from Fusion Fund investors argues the LLM scaling ...

High Energy Physics (HEP) is a deeply collaborative and software-driven discipline, where scientific discovery depends on ...

This list is about spatial intelligence for usable worlds, not just pretty renders. It prioritizes work that builds or uses structured spatial state: objects, geometry, relations, affordances, ...

Abstract: We present a sampling-based model predictive control method that uses a generic physics simulator as the dynamical model. In particular, we propose a Model Predictive Path Integral ...

Pipeline network simulations Unit conversions across SI, CGS, and Imperial systems Component-based property calculations And more, with advanced features under active development.