We introduce version 3 of NetKet, the machine learning toolbox for many-body quantum
physics. NetKet is built around neural-network quantum states and provides efficient …

Neural-network architectures have been increasingly used to represent quantum many-body
wave functions. These networks require a large number of variational parameters and are …

Physical systems are governed by partial differential equations (PDEs). The Navier-Stokes
equations describe fluid flows and are representative of nonlinear physical systems with …

We introduce a message-passing neural-network (NN)-based wave function Ansatz to
simulate extended, strongly interacting fermions in continuous space. Symmetry constraints …

Understanding the real-time evolution of many-electron quantum systems is essential for
studying dynamical properties in condensed matter, quantum chemistry, and complex …

We analyze the accuracy and sample complexity of variational Monte Carlo approaches to
simulate the dynamics of many-body quantum systems classically. By systematically …

We introduce a family of neural quantum states for the simulation of strongly interacting
systems in the presence of spatial periodicity. Our variational state is parametrized in terms …

Solving the time-dependent quantum many-body Schrödinger equation is a challenging
task, especially for states at a finite temperature, where the environment affects the …

In our effort to facilitate machine learning-assisted computational fluid dynamics (CFD), we
introduce the second iteration of JAX-Fluids. JAX-Fluids is a Python-based fully …

We develop∂-HylleraasMD (∂-HyMD), a fully end-to-end differentiable molecular dynamics
software based on the Hamiltonian hybrid particle-field formalism, and use it to establish a …