Abstract: Physics-informed neural networks (PINNs) offer a flexible framework for solving differential equations using physical constraints and data. This study focuses on second-order ...
The automatic differentiation (AD) in the vanilla physics-informed neural networks (PINNs) is the computational bottleneck for the high-efficiency analysis. The concept of derivative discretization in ...
Several fundamental results on the existence and behavior of solutions to semilinear functional differential equations are developed in a Banach space setting. The ideas are applied to ...
Blue-colored food may soon come without the usual side of petroleum and instead with a side of algae. In a major food technology breakthrough published in Food Hydrocolloids, Cornell University ...
SAN FRANCISCO--(BUSINESS WIRE)--Orchard Robotics, the AI Farming Company, today announced its oversubscribed $22M Series A funding. The round was led by Quiet Capital and Shine Capital, with continued ...
Charles Darwin's theory of evolution by natural selection made us rethink our place in the world. The idea that humans shared a common ancestor with apes was a challenge to the foundations of western ...
The ARC-AGI (Abstraction and Reasoning Corpus) is a benchmark for testing general intelligence published in 2019 by François Chollet. Specifically it tests an AI's ability to solve abstract visual ...
"Neural Networks Meet Physics: A Survey of Physics-Informed Approaches to Modeling and Simulation." Nasir, Karthika, Rahul Menon, and Sneha Iyer. (2025). [Paper] Torres, Edgar, and Mathias Niepert.
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