A review of mechanistic learning in mathematical oncology
Mechanistic learning refers to the synergistic combination of mechanistic mathematical
modeling and data-driven machine or deep learning. This emerging field finds increasing …
modeling and data-driven machine or deep learning. This emerging field finds increasing …
Residual-based error correction for neural operator accelerated infinite-dimensional Bayesian inverse problems
We explore using neural operators, or neural network representations of nonlinear maps
between function spaces, to accelerate infinite-dimensional Bayesian inverse problems …
between function spaces, to accelerate infinite-dimensional Bayesian inverse problems …
A scalable framework for multi-objective PDE-constrained design of building insulation under uncertainty
This paper introduces a scalable computational framework for optimal design under high-
dimensional uncertainty, with application to thermal insulation components. The thermal and …
dimensional uncertainty, with application to thermal insulation components. The thermal and …
A framework for strategic discovery of credible neural network surrogate models under uncertainty
The widespread integration of deep neural networks in developing data-driven surrogate
models for high-fidelity simulations of complex physical systems highlights the critical …
models for high-fidelity simulations of complex physical systems highlights the critical …
Residual-based error corrector operator to enhance accuracy and reliability of neural operator surrogates of nonlinear variational boundary-value problems
PK Jha - Computer Methods in Applied Mechanics and …, 2024 - Elsevier
This work focuses on developing methods for approximating the solution operators of a
class of parametric partial differential equations via neural operators. Neural operators have …
class of parametric partial differential equations via neural operators. Neural operators have …
Corrector Operator to Enhance Accuracy and Reliability of Neural Operator Surrogates of Nonlinear Variational Boundary-Value Problems
This work focuses on developing methods for approximating the solution operators of a
class of parametric partial differential equations via neural operators. Neural operators have …
class of parametric partial differential equations via neural operators. Neural operators have …
[PDF][PDF] Glioma Concentration Growth Simulation Using The Crank Nicolson Method.
V Noviantri, T Tjandra, R Nariswari - IAENG International Journal …, 2023 - researchgate.net
The most common type of central nervous system tumor in adults is glioma, accounting for
about 70% of all brain tumors. Prediction of glioma growth became interesting to be …
about 70% of all brain tumors. Prediction of glioma growth became interesting to be …