7 citations · 10 across the 4 of their papers we have counts for
4 papers
Operator Learning of Lipschitz Operators: An Information-Theoretic Perspective
Samuel Lanthaler
Operator learning based on neural operators has emerged as a promising paradigm for the data-driven approximation of operators, mapping between infinite-dimensional Banach spaces.…
Sharp conditions for energy balance in two-dimensional incompressible ideal flow with external force
Fabian Jin, Samuel Lanthaler, Milton C. Lopes Filho +1
Smooth solutions of the forced incompressible Euler equations satisfy an energy balance, where the rate-of-change in time of the kinetic energy equals the work done by the force pe…
Operator Learning: Algorithms and Analysis
Nikola B. Kovachki, Samuel Lanthaler, Andrew M. Stuart
Operator learning refers to the application of ideas from machine learning to approximate (typically nonlinear) operators mapping between Banach spaces of functions. Such operators…
Neural Oscillators are Universal
Samuel Lanthaler, T. Konstantin Rusch, Siddhartha Mishra
Coupled oscillators are being increasingly used as the basis of machine learning (ML) architectures, for instance in sequence modeling, graph representation learning and in physica…