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SYMMETRIC BASIS CONVOLUTIONS FOR LEARNING LAGRANGIAN FLUID MECHANICS
Rene Winchenbach,
Nils Thuerey
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Technical University of Munich
Research output
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Contribution to conference
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Paper
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peer-review
2
Scopus citations
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Dive into the research topics of 'SYMMETRIC BASIS CONVOLUTIONS FOR LEARNING LAGRANGIAN FLUID MECHANICS'. Together they form a unique fingerprint.
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Keyphrases
Fluid Mechanics
100%
SPH Simulation
100%
Lagrangian Fluids
100%
Symmetric Basis
100%
Machine Learning
66%
Fourier-based
66%
Continuous Convolution
66%
Computationally Expensive
33%
Inverse Problem
33%
Large Set
33%
Numerical Solver
33%
Physical Simulation
33%
Weakly Compressible
33%
Window Function
33%
Navier-Stokes
33%
Inductive Bias
33%
Neural PDE Solvers
33%
Odd Symmetry
33%
Computer Science
Basis Function
100%
Continuous Convolution
66%
Machine Learning
66%
Learning System
66%
Inverse Problem
33%
Research Effort
33%
Physical Simulation
33%
Window Function
33%
Mathematics
Lagrangian
100%
Convolution
100%
Basis Function
100%
Window Function
33%
Chemical Engineering
Learning System
100%