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Found 6 papers, 2 papers with code
PMBO: Enhancing Black-Box Optimization through Multivariate Polynomial Surrogates
We introduce a surrogate-based black-box optimization method, termed Polynomial-model-based optimization (PMBO).
Ensuring Topological Data-Structure Preservation under Autoencoder Compression due to Latent Space Regularization in Gauss--Legendre nodes
Revisiting this classic enables to prove that regularised autoencoders ensure a one-to-one re-embedding of the initial data manifold to its latent representation.
Polynomial-Model-Based Optimization for Blackbox Objectives
For a wide range of applications the structure of systems like Neural Networks or complex simulations, is unknown and approximation is costly or even impossible.
Learning Partial Differential Equations by Spectral Approximates of General Sobolev Spaces
In contrast to PINNs, the PSMs result in a convex optimisation problem for a vast class of PDEs, including all linear ones, in which case the PSM-approximate is efficiently computable due to the exponential convergence rate of the underlying variational gradient descent.
Replacing Automatic Differentiation by Sobolev Cubatures fastens Physics Informed Neural Nets and strengthens their Approximation Power
We present a novel class of approximations for variational losses, being applicable for the training of physics-informed neural nets (PINNs).
InFlow: Robust outlier detection utilizing Normalizing Flows
Normalizing flows are prominent deep generative models that provide tractable probability distributions and efficient density estimation.
