45 citations · 77 across the 11 of their papers we have counts for
14 papers
Information geometric regularization for computing sensitivities of flows with shocks
Florian Schäfer
Computing (adjoint) sensitivities of flows with shocks is a longstanding problem in computational fluid dynamics. The spurious sensitivities due to shock sensors and limiters frequ…
Discontinuous Galerkin Semidiscretization of the Information Geometric Regularized Compressible Euler Equations
Brook Eyob, Jesus Arias, Spencer H. Bryngelson +1
Shock stabilization in compressible Euler flows remains a central challenge for high-order numerical methods. Existing shock-capturing approaches, including limiters, artificial vi…
Shocks without shock capturing: Information geometric regularization of finite volume methods for Navier--Stokes-like problems
Anand Radhakrishnan, Benjamin Wilfong, Spencer H. Bryngelson +1
Shock waves in high-speed fluid dynamics produce near-discontinuities in the fluid momentum, density, and energy. Most contemporary works use artificial viscosity or limiters as nu…
Information geometric regularization of the barotropic Euler equation
Ruijia Cao, Florian Schäfer
Shock waves in gas dynamics feature jump discontinuities that hinder numerical simulations. Viscous regularizations are prone to excessive dissipation of fine-scale structures. In…
Fast Macroscopic Forcing Method
Spencer H. Bryngelson, Florian Schäfer, Jessie Liu +1
The macroscopic forcing method (MFM) of Mani and Park and similar methods for obtaining turbulence closure operators, such as the Green's function-based approach of Hamba, recover…
Score Operator Newton transport
Nisha Chandramoorthy, Florian Schaefer, Youssef Marzouk
We propose a new approach for sampling and Bayesian computation that uses the score of the target distribution to construct a transport from a given reference distribution to the t…
Variational sparse inverse Cholesky approximation for latent Gaussian processes via double Kullback-Leibler minimization
Jian Cao, Myeongjong Kang, Felix Jimenez +3
To achieve scalable and accurate inference for latent Gaussian processes, we propose a variational approximation based on a family of Gaussian distributions whose covariance matric…
ZerO Initialization: Initializing Neural Networks with only Zeros and Ones
Jiawei Zhao, Florian Schäfer, Anima Anandkumar
Deep neural networks are usually initialized with random weights, with adequately selected initial variance to ensure stable signal propagation during training. However, selecting…
Sparse recovery of elliptic solvers from matrix-vector products
Florian Schäfer, Houman Owhadi
In this work, we show that solvers of elliptic boundary value problems in dimensions can be approximated to accuracy from only $\mathcal{O}\left(\log(N)\log^{d}(N / ε)\righ…
Sparse Cholesky factorization by Kullback-Leibler minimization
Florian Schäfer, Matthias Katzfuss, Houman Owhadi
We propose to compute a sparse approximate inverse Cholesky factor of a dense covariance matrix by minimizing the Kullback-Leibler divergence between the Gaussian distribut…
Implicit competitive regularization in GANs
Florian Schäfer, Hongkai Zheng, Anima Anandkumar
To improve the stability of GAN training we need to understand why they can produce realistic samples. Presently, this is attributed to properties of the divergence obtained under…
Competitive Gradient Descent
Florian Schäfer, Anima Anandkumar
We introduce a new algorithm for the numerical computation of Nash equilibria of competitive two-player games. Our method is a natural generalization of gradient descent to the two…
Compression, inversion, and approximate PCA of dense kernel matrices at near-linear computational complexity
Florian Schäfer, T. J. Sullivan, Houman Owhadi
Dense kernel matrices obtained from point evaluations of a covariance function at locations $\{ x_{i} \}_{1 \leq i \leq N} \subset \mathbb{R}^{d}…
Image Extrapolation for the Time Discrete Metamorphosis Model - Existence and Applications
Alexander Effland, Martin Rumpf, Florian Schäfer
The space of images can be equipped with a Riemannian metric measuring both the cost of transport of image intensities and the variation of image intensities along motion lines. Th…