A new study demonstrates how neurons in nematode worms respond to odors and generate movement to augment understanding of neural circuits.
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Physics-trained AI models speed up engineering simulations and design work
Running a single physics simulation can take hours or days, depending on the complexity of the geometry and the equations ...
Losing your sense of smell might signal Alzheimer’s far earlier than expected. Scientists found that immune cells in the brain actively destroy smell-related nerve fibers after detecting abnormal ...
How do worms navigate? A new study maps the whole-brain circuit of C. elegans, revealing how neurons and the chemical ...
Researchers present a comprehensive review of frontier AI applications in computational structural analysis from 2020 to 2025, focusing on graph neural networks (GNNs), sequence-to-sequence (Seq2Seq) ...
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 ...
This repository contains the source code for the paper "Space Correlation Constrained Physics Informed Neural Network for Seismic Tomography", accepted by JGR: Machine Learning and Computation on ...
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 ...
Accurate joint kinematics estimation is essential for understanding human movement and supporting biomechanical applications. Although optical motion capture systems are accurate, their high cost, ...
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