Top Tweets for #physics_comp_ph
[2301.06595] Lars Loetgering, Mengqi Du, Dirk Boonzajer Flaes et al.: PtyLab.m/py/jl: a cross-platform, open-source inverse modeling toolbox for conventional and Fourier ptychography https://t.co/5hoCo5crvI https://t.co/VNKlfcdf2v #physics_comp_ph
[2301.02191] Fatemeh Hafezianzade, Morad Biagooi, SeyedEhsan Nedaaee Oskoee: Physics informed neural network for charged particles surrounded by conductive boundaries https://t.co/9zFPI14LDe https://t.co/yjA5KENkGq #physics_comp_ph
[2212.08903] Zhichao Lin, Rui Guo, Tao Shan et al.: Analysis of Degrees of Freedom in Scattered Fields for Nonlinear Inverse Scattering Problems https://t.co/ztwBKbzqCO https://t.co/L7iQiwSKGB #physics_comp_ph
[2206.11294] Youssef Saade, Detlef Lohse, Daniel Fuster: A multigrid solver for the coupled pressure-temperature equations in an all-Mach solver with VoF https://t.co/qHEOf4Wtu2 https://t.co/3oZsUXLJE9 #physics_comp_ph
[2201.00098] Megan C. Engel, Jamie A. Smith, Michael P. Brenner: Optimal control of nonequilibrium systems through automatic differentiation https://t.co/f6Tjwtm8sm https://t.co/tzWCoXUy8c #physics_comp_ph
[2107.06029] Nikolaj Rørbæk Knøsgaard, Kristian Sommer Thygesen: Representing individual electronic states in crystals for machine learning quasiparticle band structures https://t.co/GykeTR1gR8 https://t.co/2pZIQcr6ln #physics_comp_ph
[2010.01628] Alessandro De Rosis, Christophe Coreixas: Multiphysics flow simulations using D3Q19 lattice Boltzmann methods based on central moments https://t.co/nSyFjshKYH https://t.co/KyQljJw20X #physics_comp_ph
[2010.00072] Jaideep Pathak, Mustafa Mustafa, Karthik Kashinath et al.: Using Machine Learning to Augment Coarse-Grid Computational Fluid Dynamics Simulations https://t.co/Jm5Skvg3V2 https://t.co/M5D6pBCcA9 #physics_comp_ph
[2009.06533] G.P. Purja Pun, V. Yamakov, J. Hickman et al.: Development of a general-purpose machine-learning interatomic potential for aluminum by the physically-informed neural network method https://t.co/dzKhLBect9 https://t.co/rtk3DLcIzV #physics_comp_ph
[2008.08049] Wassime Siguerdidjane, Farbod Khameneifar, Frédérick P. Gosselin: Efficient planning of peen-forming patterns via artificial neural networks https://t.co/B0QKgIOngs https://t.co/wLIfqrTNao #physics_comp_ph
[2005.10723] John McFarland, Efstratios Manousakis: Imaginary-time time-dependent density functional theory for periodic systems https://t.co/BEuteLksOI https://t.co/18dkUbudXc #physics_comp_ph
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