On scientific understanding with artificial intelligence
An oracle that correctly predicts the outcome of every particle physics experiment, the
products of every possible chemical reaction or the function of every protein would …
products of every possible chemical reaction or the function of every protein would …
Modern applications of machine learning in quantum sciences
In these Lecture Notes, we provide a comprehensive introduction to the most recent
advances in the application of machine learning methods in quantum sciences. We cover …
advances in the application of machine learning methods in quantum sciences. We cover …
Replacing neural networks by optimal analytical predictors for the detection of phase transitions
Identifying phase transitions and classifying phases of matter is central to understanding the
properties and behavior of a broad range of material systems. In recent years, machine …
properties and behavior of a broad range of material systems. In recent years, machine …
Interpretable machine learning guided by physical mechanisms reveals drivers of runoff under dynamic land use changes
S Wang, Y Liu, W Wang, G Zhao, H Liang - Journal of Environmental …, 2024 - Elsevier
Human activities continuously impact water balances and cycling in watersheds, making it
essential to accurately identify the responses of runoff to dynamic changes in land use types …
essential to accurately identify the responses of runoff to dynamic changes in land use types …
Fluctuation based interpretable analysis scheme for quantum many-body snapshots
Microscopically understanding and classifying phases of matter is at the heart of strongly-
correlated quantum physics. With quantum simulations, genuine projective measurements …
correlated quantum physics. With quantum simulations, genuine projective measurements …
Deep quantum graph dreaming: deciphering neural network insights into quantum experiments
Despite their promise to facilitate new scientific discoveries, the opaqueness of neural
networks presents a challenge in interpreting the logic behind their findings. Here, we use a …
networks presents a challenge in interpreting the logic behind their findings. Here, we use a …
Characterizing out-of-distribution generalization of neural networks: application to the disordered Su–Schrieffer–Heeger model
Abstract Machine learning (ML) is a promising tool for the detection of phases of matter.
However, ML models are also known for their black-box construction, which hinders …
However, ML models are also known for their black-box construction, which hinders …
Learning minimal representations of stochastic processes with variational autoencoders
Stochastic processes have found numerous applications in science, as they are broadly
used to model a variety of natural phenomena. Due to their intrinsic randomness and …
used to model a variety of natural phenomena. Due to their intrinsic randomness and …
Synergistic eigenanalysis of covariance and Hessian matrices for enhanced binary classification
Covariance and Hessian matrices have been analyzed separately in the literature for
classification problems. However, integrating these matrices has the potential to enhance …
classification problems. However, integrating these matrices has the potential to enhance …
Speak so a physicist can understand you! TetrisCNN for detecting phase transitions and order parameters
Recently, neural networks (NNs) have become a powerful tool for detecting quantum phases
of matter. Unfortunately, NNs are black boxes and only identify phases without elucidating …
of matter. Unfortunately, NNs are black boxes and only identify phases without elucidating …