Machine learning and the physical sciences
Machine learning (ML) encompasses a broad range of algorithms and modeling tools used
for a vast array of data processing tasks, which has entered most scientific disciplines in …
for a vast array of data processing tasks, which has entered most scientific disciplines in …
An overview on deep learning-based approximation methods for partial differential equations
It is one of the most challenging problems in applied mathematics to approximatively solve
high-dimensional partial differential equations (PDEs). Recently, several deep learning …
high-dimensional partial differential equations (PDEs). Recently, several deep learning …
Discovering physical concepts with neural networks
Despite the success of neural networks at solving concrete physics problems, their use as a
general-purpose tool for scientific discovery is still in its infancy. Here, we approach this …
general-purpose tool for scientific discovery is still in its infancy. Here, we approach this …
Artificial intelligence for science in quantum, atomistic, and continuum systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
Algorithms for solving high dimensional PDEs: from nonlinear Monte Carlo to machine learning
In recent years, tremendous progress has been made on numerical algorithms for solving
partial differential equations (PDEs) in a very high dimension, using ideas from either …
partial differential equations (PDEs) in a very high dimension, using ideas from either …
Quantum state tomography with conditional generative adversarial networks
Quantum state tomography (QST) is a challenging task in intermediate-scale quantum
devices. Here, we apply conditional generative adversarial networks (CGANs) to QST. In the …
devices. Here, we apply conditional generative adversarial networks (CGANs) to QST. In the …
Variational quantum Monte Carlo method with a neural-network ansatz for open quantum systems
A Nagy, V Savona - Physical review letters, 2019 - APS
The possibility to simulate the properties of many-body open quantum systems with a large
number of degrees of freedom (dof) is the premise to the solution of several outstanding …
number of degrees of freedom (dof) is the premise to the solution of several outstanding …
Symmetries and many-body excitations with neural-network quantum states
Artificial neural networks have been recently introduced as a general ansatz to represent
many-body wave functions. In conjunction with variational Monte Carlo calculations, this …
many-body wave functions. In conjunction with variational Monte Carlo calculations, this …
Quantum entanglement in deep learning architectures
Modern deep learning has enabled unprecedented achievements in various domains.
Nonetheless, employment of machine learning for wave function representations is focused …
Nonetheless, employment of machine learning for wave function representations is focused …
Machine learning approximation algorithms for high-dimensional fully nonlinear partial differential equations and second-order backward stochastic differential …
High-dimensional partial differential equations (PDEs) appear in a number of models from
the financial industry, such as in derivative pricing models, credit valuation adjustment …
the financial industry, such as in derivative pricing models, credit valuation adjustment …