Learning High-Dimensional Nonparametric Differential Equations via Multivariate Occupation Kernel Functions

• URL: http://arxiv.org/abs/2306.10189v1
• Date: Fri, 16 Jun 2023 21:49:36 GMT
• Title: Learning High-Dimensional Nonparametric Differential Equations via Multivariate Occupation Kernel Functions
• Authors: Victor Rielly, Kamel Lahouel, Ethan Lew, Michael Wells, Vicky Haney, Bruno Jedynak
• Abstract summary: Learning a nonparametric system of ordinary differential equations requires learning $d$ functions of $d$ variables. Explicit formulations scale quadratically in $d$ unless additional knowledge about system properties, such as sparsity and symmetries, is available. We propose a linear approach to learning using the implicit formulation provided by vector-valued Reproducing Kernel Hilbert Spaces.
• Score: 0.31317409221921133
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Learning a nonparametric system of ordinary differential equations (ODEs) from $n$ trajectory snapshots in a $d$-dimensional state space requires learning $d$ functions of $d$ variables. Explicit formulations scale quadratically in $d$ unless additional knowledge about system properties, such as sparsity and symmetries, is available. In this work, we propose a linear approach to learning using the implicit formulation provided by vector-valued Reproducing Kernel Hilbert Spaces. By rewriting the ODEs in a weaker integral form, which we subsequently minimize, we derive our learning algorithm. The minimization problem's solution for the vector field relies on multivariate occupation kernel functions associated with the solution trajectories. We validate our approach through experiments on highly nonlinear simulated and real data, where $d$ may exceed 100. We further demonstrate the versatility of the proposed method by learning a nonparametric first order quasilinear partial differential equation.

Related papers

• A backward differential deep learning-based algorithm for solving high-dimensional nonlinear backward stochastic differential equations [0.6040014326756179]
  We propose a novel backward differential deep learning-based algorithm for solving high-dimensional nonlinear backward differential equations. The deep neural network (DNN) models are trained not only on the inputs and labels but also the differentials of the corresponding labels.
  arXiv  Detail & Related papers  (2024-04-12T13:05:35Z)
• Physics-Informed Quantum Machine Learning: Solving nonlinear differential equations in latent spaces without costly grid evaluations [21.24186888129542]
  We propose a physics-informed quantum algorithm to solve nonlinear and multidimensional differential equations. By measuring the overlaps between states which are representations of DE terms, we construct a loss that does not require independent sequential function evaluations on grid points. When the loss is trained variationally, our approach can be related to the differentiable quantum circuit protocol.
  arXiv  Detail & Related papers  (2023-08-03T15:38:31Z)
• Deep Learning Approximation of Diffeomorphisms via Linear-Control Systems [91.3755431537592]
  We consider a control system of the form $dot x = sum_i=1lF_i(x)u_i$, with linear dependence in the controls. We use the corresponding flow to approximate the action of a diffeomorphism on a compact ensemble of points.
  arXiv  Detail & Related papers  (2021-10-24T08:57:46Z)
• Learning Linearized Assignment Flows for Image Labeling [70.540936204654]
  We introduce a novel algorithm for estimating optimal parameters of linearized assignment flows for image labeling. We show how to efficiently evaluate this formula using a Krylov subspace and a low-rank approximation.
  arXiv  Detail & Related papers  (2021-08-02T13:38:09Z)
• Multiscale regression on unknown manifolds [13.752772802705978]
  We construct low-dimensional coordinates on $mathcalM$ at multiple scales and perform multiscale regression by local fitting. We analyze the generalization error of our method by proving finite sample bounds in high probability on rich classes of priors. Our algorithm has quasilinear complexity in the sample size, with constants linear in $D$ and exponential in $d$.
  arXiv  Detail & Related papers  (2021-01-13T15:14:31Z)
• Deep neural network for solving differential equations motivated by Legendre-Galerkin approximation [16.64525769134209]
  We explore the performance and accuracy of various neural architectures on both linear and nonlinear differential equations. We implement a novel Legendre-Galerkin Deep Neural Network (LGNet) algorithm to predict solutions to differential equations.
  arXiv  Detail & Related papers  (2020-10-24T20:25:09Z)
• Fourier Neural Operator for Parametric Partial Differential Equations [57.90284928158383]
  We formulate a new neural operator by parameterizing the integral kernel directly in Fourier space. We perform experiments on Burgers' equation, Darcy flow, and Navier-Stokes equation. It is up to three orders of magnitude faster compared to traditional PDE solvers.
  arXiv  Detail & Related papers  (2020-10-18T00:34:21Z)
• Large-time asymptotics in deep learning [0.0]
  We consider the impact of the final time $T$ (which may indicate the depth of a corresponding ResNet) in training. For the classical $L2$--regularized empirical risk minimization problem, we show that the training error is at most of the order $mathcalOleft(frac1Tright)$. In the setting of $ellp$--distance losses, we prove that both the training error and the optimal parameters are at most of the order $mathcalOleft(e-mu
  arXiv  Detail & Related papers  (2020-08-06T07:33:17Z)
• Piecewise Linear Regression via a Difference of Convex Functions [50.89452535187813]
  We present a new piecewise linear regression methodology that utilizes fitting a difference of convex functions (DC functions) to the data. We empirically validate the method, showing it to be practically implementable, and to have comparable performance to existing regression/classification methods on real-world datasets.
  arXiv  Detail & Related papers  (2020-07-05T18:58:47Z)
• Learning nonlinear dynamical systems from a single trajectory [102.60042167341956]
  We introduce algorithms for learning nonlinear dynamical systems of the form $x_t+1=sigma(Thetastarx_t)+varepsilon_t$. We give an algorithm that recovers the weight matrix $Thetastar$ from a single trajectory with optimal sample complexity and linear running time.
  arXiv  Detail & Related papers  (2020-04-30T10:42:48Z)
• Complexity of Finding Stationary Points of Nonsmooth Nonconvex Functions [84.49087114959872]
  We provide the first non-asymptotic analysis for finding stationary points of nonsmooth, nonsmooth functions. In particular, we study Hadamard semi-differentiable functions, perhaps the largest class of nonsmooth functions.
  arXiv  Detail & Related papers  (2020-02-10T23:23:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.