A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
W. Yeadon, Alex Peach, Craig P. Testrow
Evaluates ChatGPT variants on university-level physics coding assignments, finding students outperform AI and human evaluators detect AI work with 85.3% accuracy.
Florian Wiesner, Matthias Wessling, Stephen Baek
A single transformer model trained on diverse simulation data demonstrates foundation model capabilities for physics, generalizing across domains without retraining.
Luzhe Huang, Hanlong Chen, Tairan Liu, et al.
GedankenNet eliminates the need for labeled or experimental training data in hologram reconstruction by using a physics-consistency loss and synthetic random images.
Matthew R. Masters, Amr H. Mahmoud, Markus A. Lill
This paper reveals that deep learning co-folding models for protein-ligand structure prediction often violate fundamental physical principles, indicating overfitting rather than learning true physics.
Logan G. Wright, Tatsuhiro Onodera, Martin M. Stein, et al.
Introduces physics-aware training, a hybrid algorithm that applies backpropagation to train deep physical neural networks made from diverse physical substrates like optics, mechanics, and electronics.
Hongxin Zhang, Chunru Lin, Junyan Li, et al.
GS-Agent is a multi-agent framework that automates the creation of physically plausible 4D worlds from natural language by integrating physics engines and emulating human workflows.
Kishor Bharti, Alba Cervera-Lierta, Thi Ha Kyaw, et al.
This review surveys algorithms and applications for noisy intermediate-scale quantum (NISQ) computers, covering many-body physics, chemistry, optimization, and machine learning.