Many steady-state transport problems in condensed matter physics can be reduced to a set of coupled diffusion equations. This is true in particular when relaxation processes are sufficiently fast that the system is in the diffusive --opposite of ballistic-- regime. Here we describe a python package, based on FEniCS, that solves this type of problems with an arbitrary number degrees of freedom that can represent charge, spin, energy, band or valley flavours. Generalized conductivities and responsivities, characterizing completely the linear response of the system to external biases and sources, are automatically computed from the solutions. We solve two simple example of magneto-transport and thermoelectric transport for illustrative purpose.
A Constrained Natural-Language Interface for Variational Multi-Physics Finite Element Simulations in FEniCS
Department of Engineering Science and Mechanics, The Pennsylvania State University（工程科学与力学系，宾夕法尼亚州立大学）；Department of Materials Science and Engineering, The Pennsylvania State University（材料科学与工程系，宾夕法尼亚州立大学）
Physics Enhanced Deep Surrogates for the Phonon Boltzmann Transport Equation
School of Computational Science and Engineering, Georgia Institute of Technology（计算科学与工程学院，佐治亚理工学院）；Institute for Soldier Nanotechnologies, Massachusetts Institute of Technology（士兵纳米技术研究所，麻省理工学院）
Learning the Exact Flux: Neural Riemann Solvers with Hard Constraints
Oden Institute for Computational Engineering and Sciences, University of Texas at Austin（德克萨斯大学奥斯汀分校奥登计算工程与科学研究所）；National Center for Atmospheric Research（美国国家大气研究中心）；Department of Aerospace Engineering and Engineering Mechanics, University of Texas at Austin（德克萨斯大学奥斯汀分校航空航天工程与应用力学系）
