Integrating scientific knowledge with machine learning for engineering and environmental systems
There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …
require novel methodologies that are able to integrate traditional physics-based modeling …
Wavelet transform application for/in non-stationary time-series analysis: A review
Non-stationary time series (TS) analysis has gained an explosive interest over the recent
decades in different applied sciences. In fact, several decomposition methods were …
decades in different applied sciences. In fact, several decomposition methods were …
Multimodal data integration using machine learning improves risk stratification of high-grade serous ovarian cancer
KM Boehm, EA Aherne, L Ellenson, I Nikolovski… - Nature cancer, 2022 - nature.com
Patients with high-grade serous ovarian cancer suffer poor prognosis and variable response
to treatment. Known prognostic factors for this disease include homologous recombination …
to treatment. Known prognostic factors for this disease include homologous recombination …
Multiwavelet-based operator learning for differential equations
The solution of a partial differential equation can be obtained by computing the inverse
operator map between the input and the solution space. Towards this end, we introduce a …
operator map between the input and the solution space. Towards this end, we introduce a …
[PDF][PDF] Integrating physics-based modeling with machine learning: A survey
There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …
require novel methodologies that are able to integrate traditional physics-based modeling …
[图书][B] Data-driven science and engineering: Machine learning, dynamical systems, and control
SL Brunton, JN Kutz - 2022 - books.google.com
Data-driven discovery is revolutionizing how we model, predict, and control complex
systems. Now with Python and MATLAB®, this textbook trains mathematical scientists and …
systems. Now with Python and MATLAB®, this textbook trains mathematical scientists and …
A wavelet based numerical scheme for fractional order SEIR epidemic of measles by using Genocchi polynomials
Epidemiology is the glorious discipline underlying medical research, public health practice,
and health care evaluation. Nowadays, research on disease models with anonymous …
and health care evaluation. Nowadays, research on disease models with anonymous …
An efficient numerical method for fractional SIR epidemic model of infectious disease by using Bernstein wavelets
In this paper, the operational matrix based on Bernstein wavelets is presented for solving
fractional SIR model with unknown parameters. The SIR model is a system of differential …
fractional SIR model with unknown parameters. The SIR model is a system of differential …
An iterative thresholding algorithm for linear inverse problems with a sparsity constraint
I Daubechies, M Defrise… - Communications on Pure …, 2004 - Wiley Online Library
We consider linear inverse problems where the solution is assumed to have a sparse
expansion on an arbitrary preassigned orthonormal basis. We prove that replacing the usual …
expansion on an arbitrary preassigned orthonormal basis. We prove that replacing the usual …