Implementing physics-informed neural networks with deep learning for differential equations. [PDF]
Front Artif IntellEmmert-Streib F +3 more
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A novel hybrid framework for efficient higher order ODE solvers using neural networks and block methods. [PDF]
Sci RepMurugesh V +7 more
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Computational analysis of stochastic delay dynamics in maize streak virus. [PDF]
PLoS OneIqbal S +6 more
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Physics-informed residual learning with spatiotemporal local support for inverse ECG reconstruction. [PDF]
Sci RepZhu L, Bilchick K, Xie J.
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Solving PDEs with random data by stochastic collocation
, 2013 In many science and engineering problems there is uncertainty in the input data. The ability to suitably model and handle this uncertainty is crucial for obtaining meaningful information about solutions. In this thesis, we consider the numerical approximation of statistics of solutions to partial differential equations (PDEs) with uncertain inputs.
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Computational investigation of stochastic Zika virus optimal control model using Legendre spectral method. [PDF]
Sci RepZhu J +5 more
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A Random Differential Equation Approach for Modeling the Growth of Microalgae in Photobioreactors. [PDF]
Bull Math BiolAndreu-Vilarroig C +4 more
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A physics-informed neural network approach for estimating population-level pharmacokinetic parameters from aggregated concentration data. [PDF]
J Pharmacokinet PharmacodynTsiros P, Minadakis V, Sarimveis H.
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Numerical simulation of a fractional stochastic delay differential equations using spectral scheme: a comprehensive stability analysis. [PDF]
Sci RepLi S +4 more
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