Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SINDy-BVP: Sparse Identification of Nonlinear Dynamics for Boundary
  Value Problems   We develop a data-driven model discovery and system identification technique
for spatially-dependent boundary value problems (BVPs). Specifically, we
leverage the sparse identification of nonlinear dynamics (SINDy) algorithm and
group sparse regression techniques with a set of forcing functions and
corresponding state variable measurements to yield a parsimonious model of the
system. The approach models forced systems governed by linear or nonlinear
operators of the form $L[u(x)] = f(x)$ on a prescribed domain $x \in [a, b]$.
We demonstrate the approach on a range of example systems, including
Sturm-Liouville operators, beam theory (elasticity), and a class of nonlinear
BVPs. The generated data-driven model is used to infer both the operator and/or
spatially-dependent parameters that describe the heterogenous, physical
quantities of the system. Our SINDy-BVP framework will enables the
characterization of a broad range of systems, including for instance, the
discovery of anisotropic materials with heterogeneous variability.

16
The categories of this paper are: stat
29
The categories of this paper are: funct-an
21
The categories of this paper are: adap-org
0
The categories of this paper are: hep-ph
25
The categories of this paper are: patt-sol
13
The categories of this paper are: quant-ph
22
The categories of this paper are: chao-dyn
6
The categories of this paper are: astro-ph
4
The categories of this paper are: cond-mat
23
The categories of this paper are: solv-int
18
The categories of this paper are: econ
24
The categories of this paper are: dg-ga
31
The categories of this paper are: ao-sci
32
The categories of this paper are: atom-ph
3
The categories of this paper are: physics
33
The categories of this paper are: plasm-ph