Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design
One of the main challenges in materials discovery is efficiently exploring the vast search
space for targeted properties as approaches that rely on trial-and-error are impractical. We …
space for targeted properties as approaches that rely on trial-and-error are impractical. We …
Modern Monte Carlo methods for efficient uncertainty quantification and propagation: A survey
J Zhang - Wiley Interdisciplinary Reviews: Computational …, 2021 - Wiley Online Library
Uncertainty quantification (UQ) includes the characterization, integration, and propagation of
uncertainties that result from stochastic variations and a lack of knowledge or data in the …
uncertainties that result from stochastic variations and a lack of knowledge or data in the …
fPINNs: Fractional physics-informed neural networks
Physics-informed neural networks (PINNs), introduced in M. Raissi, P. Perdikaris, and G.
Karniadakis, J. Comput. Phys., 378 (2019), pp. 686--707, are effective in solving integer …
Karniadakis, J. Comput. Phys., 378 (2019), pp. 686--707, are effective in solving integer …
nPINNs: nonlocal Physics-Informed Neural Networks for a parametrized nonlocal universal Laplacian operator. Algorithms and Applications
Physics-informed neural networks (PINNs) are effective in solving inverse problems based
on differential and integro-differential equations with sparse, noisy, unstructured, and multi …
on differential and integro-differential equations with sparse, noisy, unstructured, and multi …
Identifiability and predictability of integer-and fractional-order epidemiological models using physics-informed neural networks
We analyze a plurality of epidemiological models through the lens of physics-informed
neural networks (PINNs) that enable us to identify time-dependent parameters and data …
neural networks (PINNs) that enable us to identify time-dependent parameters and data …
Applying Bayesian optimization with Gaussian process regression to computational fluid dynamics problems
Bayesian optimization (BO) based on Gaussian process regression (GPR) is applied to
different CFD (computational fluid dynamics) problems which can be of practical relevance …
different CFD (computational fluid dynamics) problems which can be of practical relevance …
Wellposedness and regularity of the variable-order time-fractional diffusion equations
It was proved that the solution to a time-fractional partial differential equation lacks regularity
at the initial time t= 0, which is often inconsistent with the physical problem being modeled …
at the initial time t= 0, which is often inconsistent with the physical problem being modeled …
Neural-net-induced Gaussian process regression for function approximation and PDE solution
Neural-net-induced Gaussian process (NNGP) regression inherits both the high expressivity
of deep neural networks (deep NNs) as well as the uncertainty quantification property of …
of deep neural networks (deep NNs) as well as the uncertainty quantification property of …
Fractional modeling in action: A survey of nonlocal models for subsurface transport, turbulent flows, and anomalous materials
Modeling of phenomena such as anomalous transport via fractional-order differential
equations has been established as an effective alternative to partial differential equations …
equations has been established as an effective alternative to partial differential equations …
Parameter estimation for unsteady MHD oscillatory free convective flow of generalized second grade fluid with Hall effects and thermal radiation effects
S Wang, H Zhang, X Jiang - International Journal of Heat and Mass …, 2024 - Elsevier
This work first establishes an unsteady magnetohydrodynamic (MHD) oscillatory free
convection flow model of the generalized second grade fluid with Hall heat and mass …
convection flow model of the generalized second grade fluid with Hall heat and mass …