IDENT: Identifying Differential Equations with Numerical Time Evolution
نویسندگان
چکیده
Identifying unknown differential equations from a given set of discrete time dependent data is challenging problem. A small amount noise can make the recovery unstable. Nonlinearity and varying coefficients add complexity to We assume that governing partial equation (PDE) linear combination few terms in prescribed dictionary, objective this paper find correct coefficients. propose new direction based on fundamental convergence principle numerical PDE schemes. utilize Lasso for efficiency, performance guarantee established an incoherence property. The main contribution validate results by evolution error (TEE). algorithm, called identifying with (IDENT), explored non-periodic boundary conditions, noisy PDEs Based theory Lasso, we definition Noise-to-Signal ratio, which better represents level case identification. effects generations downsampling are systematically analyzed tested. For data, order preserving denoising method least-squares moving average (LSMA), preprocess data. identification coefficients, Base Element Expansion (BEE) aid computation. Various experiments basic tests presented.
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ژورنال
عنوان ژورنال: Journal of Scientific Computing
سال: 2021
ISSN: ['1573-7691', '0885-7474']
DOI: https://doi.org/10.1007/s10915-020-01404-9