
DOCS . NVIDIA . COM {
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NVIDIA PhysicsNeMo Release Notes - NVIDIA Docs
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Keywords {๐}
training, model, network, models, modulus, loss, physics, sym, neural, features, based, featureshighlights, architectures, support, data, recipes, examples, nvidia, enhancements, weather, global, architecture, distributed, cuda, external, problems, geometry, flow, feature, fno, fourier, operator, optimizers, physicsnemo, domino, details, recipe, corrdiff, sample, networks, graphcast, utilities, deeponet, boundary, aerodynamics, version, improved, container, diffusion, pytorch,
Topics {โ๏ธ}
fully connected networks neural network architectures branch/trunk net modified multi-gpu/multi-node implementations causal weighting scheme temporal multi-head attention neural network solution l1 loss decay arbitrary network architectures physics-informed 1d/2d problems network architectures multi-modal observation data nvidia physicsnemo team kolmogorovโarnold networks meshless finite derivatives modulus sym networks meshless finite difference vanilla physics-informed implementations leverages cuda graphs km-scale atmospheric downscaling extend data-driven models physics-informed problems based linear-exponential learning rate approximate differentiation approach high resolution images relative loss balancing emulate convection-allowing models explicitly physics-informed version physics-based benchmark suite k-epsilon & k-omega loss balancing algorithms convolutional rnn-based models temporal correlations grad norm multi-scale equation turbulence operator learning phase cuda graphs neural tangent kernel creating training/testing datasets multi-level mesh encoding beta feature structural mechanics domain km scale conditioned multi-gpu training data-driven problems boundary condition aware method
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