9 citations · 46 across the 17 of their papers we have counts for
21 papers
Counterfactual Predictions in Scientific Emulators Without Controlled Experiments
Dingling Yao, Kahaan Gandhi, Valentin Duruisseaux +3
Many scientific questions require reasoning about what was never observed: What if the conditions, interventions, or history had been different? Models can predict accurately on ob…
AI-Accelerated Gyrokinetic Predictions of Turbulent Transport for Stellarator Design Optimization and Experimental Planning
R. Michael Churchill, Matt Landreman, Jong Youl Choi +10
Previous work built AI-based surrogates for a nonlinear gyrokinetic simulation code with the goal of using them for fast, direct calculations of turbulent ion heat flux in stellara…
Inverse Design of Quantum Control Sequences with Fourier Neural Operators
Anastasia Pipi, Valentin Duruisseaux, Emily Been +4
Quantum optimal control is a key tool for steering quantum dynamics, but its computational cost grows rapidly with the Hilbert space dimension. Here, we introduce a Fourier Neural…
Fourier Neural Operators Explained: A Practical Perspective
Valentin Duruisseaux, Jean Kossaifi, Anima Anandkumar
Partial differential equations (PDEs) govern a wide variety of dynamical processes in science and engineering, yet obtaining their numerical solutions often requires high-resolutio…
Principled Approaches for Extending Neural Architectures to Function Spaces for Operator Learning
Julius Berner, Miguel Liu-Schiaffini, Jean Kossaifi +4
A wide range of scientific problems, such as those described by continuous-time dynamical systems and partial differential equations (PDEs), are naturally formulated on function sp…
NOBLE -- Neural Operator with Biologically-informed Latent Embeddings to Capture Experimental Variability in Biological Neuron Models
Luca Ghafourpour, Valentin Duruisseaux, Bahareh Tolooshams +3
Characterizing the cellular properties of neurons is fundamental to understanding their function in the brain. In this quest, the generation of bio-realistic models is central towa…
Enabling Automatic Differentiation with Mollified Graph Neural Operators
Ryan Y. Lin, Julius Berner, Valentin Duruisseaux +5
Physics-informed neural operators offer a powerful framework for learning solution operators of partial differential equations (PDEs) by combining data and physics losses. However,…
A Library for Learning Neural Operators
Jean Kossaifi, Nikola Kovachki, Zongyi Li +8
We present NeuralOperator, an open-source Python library for operator learning. Neural operators generalize neural networks to maps between function spaces instead of finite-dimens…
Projected Neural Differential Equations for Learning Constrained Dynamics
Alistair White, Anna Büttner, Maximilian Gelbrecht +4
Neural differential equations offer a powerful approach for learning dynamical systems from data. However, they do not inherently respect known constraints, such as conservation la…
An Operator Learning Framework for Spatiotemporal Super-resolution of Scientific Simulations
Valentin Duruisseaux, Amit Chakraborty
In numerous contexts, high-resolution solutions to partial differential equations are required to capture faithfully essential dynamics which occur at small spatiotemporal scales,…
Simplifying Momentum-based Positive-definite Submanifold Optimization with Applications to Deep Learning
Wu Lin, Valentin Duruisseaux, Melvin Leok +3
Riemannian submanifold optimization with momentum is computationally challenging because, to ensure that the iterates remain on the submanifold, we often need to solve difficult di…
Lie Group Forced Variational Integrator Networks for Learning and Control of Robot Systems
Valentin Duruisseaux, Thai Duong, Melvin Leok +1
Incorporating prior knowledge of physics laws and structural properties of dynamical systems into the design of deep learning architectures has proven to be a powerful technique fo…
FC-PINO: High Precision Physics-Informed Neural Operators via Fourier Continuation
Adarsh Ganeshram, Haydn Maust, Valentin Duruisseaux +6
The physics-informed neural operator (PINO) is a machine learning paradigm that has demonstrated promising results for learning solutions to partial differential equations (PDEs).…
Approximation of nearly-periodic symplectic maps via structure-preserving neural networks
Valentin Duruisseaux, Joshua W. Burby, Qi Tang
A continuous-time dynamical system with parameter is nearly-periodic if all its trajectories are periodic with nowhere-vanishing angular frequency as ap…
Practical Perspectives on Symplectic Accelerated Optimization
Valentin Duruisseaux, Melvin Leok
Geometric numerical integration has recently been exploited to design symplectic accelerated optimization algorithms by simulating the Lagrangian and Hamiltonian systems from the v…
Bistability, Bifurcations and Chaos in the Mackey-Glass Equation
Valentin Duruisseaux, Antony R. Humphries
Numerical bifurcation analysis, and in particular two-parameter continuation, is used in consort with numerical simulation to reveal complicated dynamics in the Mackey-Glass equati…
Accelerated Optimization on Riemannian Manifolds via Projected Variational Integrators
Valentin Duruisseaux, Melvin Leok
A variational formulation of accelerated optimization on normed spaces was recently introduced by considering a specific family of time-dependent Bregman Lagrangian and Hamiltonian…
Time-adaptive Lagrangian Variational Integrators for Accelerated Optimization on Manifolds
Valentin Duruisseaux, Melvin Leok
A variational framework for accelerated optimization was recently introduced on normed vector spaces and Riemannian manifolds in Wibisono et al. (2016) and Duruisseaux and Leok (20…
Accelerated Optimization on Riemannian Manifolds via Discrete Constrained Variational Integrators
Valentin Duruisseaux, Melvin Leok
A variational formulation for accelerated optimization on normed vector spaces was recently introduced in Wibisono et al., and later generalized to the Riemannian manifold setting…
A Variational Formulation of Accelerated Optimization on Riemannian Manifolds
Valentin Duruisseaux, Melvin Leok
It was shown recently by Su et al. (2016) that Nesterov's accelerated gradient method for minimizing a smooth convex function can be thought of as the time discretization of a…
Adaptive Hamiltonian Variational Integrators and Symplectic Accelerated Optimization
Valentin Duruisseaux, Jeremy Schmitt, Melvin Leok
It is well known that symplectic integrators lose their near energy preservation properties when variable step sizes are used. The most common approach to combine adaptive step siz…