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Found 8 papers, 1 papers with code
A Thermal Machine Learning Solver For Chip Simulation
Thermal analysis provides deeper insights into electronic chips behavior under different temperature scenarios and enables faster design exploration.
A composable autoencoder-based iterative algorithm for accelerating numerical simulations
Numerical simulations for engineering applications solve partial differential equations (PDE) to model various physical processes.
A composable autoencoder-based algorithm for accelerating numerical simulations
Numerical simulations for engineering applications solve partial differential equations (PDE) to model various physical processes.
Geometry encoding for numerical simulations
We present a notion of geometry encoding suitable for machine learning-based numerical simulation.
A Latent space solver for PDE generalization
In this work we propose a hybrid solver to solve partial differential equation (PDE)s in the latent space.
One-shot learning for solution operators of partial differential equations
Discovering governing equations of a physical system, represented by partial differential equations (PDEs), from data is a central challenge in a variety of areas of science and engineering.
Active Deep Learning on Entity Resolution by Risk Sampling
Building upon the recent advances in risk analysis for ER, which can provide a more refined estimate on label misprediction risk than the simpler classifier outputs, we propose a novel AL approach of risk sampling for ER.
An unsupervised learning approach to solving heat equations on chip based on Auto Encoder and Image Gradient
Specifically, a hybrid framework of Auto Encoder (AE) and Image Gradient (IG) based network is designed.
