A Posteriori Error‐Driven Space‐Time Adaptive FEM for Accurate Avascular Tumor and Drug Interaction Modeling

ABSTRACT This study presents a space‐time adaptive finite element method (FEM) integrated with a posteriori error analysis to simulate avascular tumor growth and drug interactions in prostate cancer. The model is based on a nonlinear, coupled system of reaction‐diffusion equations, incorporating discontinuities in time to represent drug interactions. We perform a residual‐based a posteriori error analysis, providing precise error estimates for both spatial and temporal components. The approach numerically ensures boundedness and positivity for the phase variable, nutrient, and prostate‐specific antigen (PSA) concentrations. Furthermore, we establish and prove the reliable error estimators for space and time discretization. Numerical tests, including cases with and without drug interactions, show that the estimators accurately capture tumor growth. A comparison of solution convergence using uniform grids and Adaptive Mesh Refinement (AMR) demonstrates the superior efficiency of AMR, significantly reducing computational effort in the absence of drug effects and achieving a substantial reduction in computational costs when drug effects are present. This highlights the effectiveness of AMR in optimizing both accuracy and computational efficiency, making it an essential tool for modeling such complex systems.

Authors

Institutions

Publication Details

Journal
Numerical Methods for Partial Differential Equations
Published
2026-02-25
DOI
https://doi.org/10.1002/num.70085
Primary Topic
Mathematical Biology Tumor Growth
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Posteriori Error‐Driven Space‐Time Adaptive FEM for Accurate Avascular Tumor and Drug Interaction Modeling

Vivek S. Yadav, Gobinda Garai, Nishant Ranwan, Nagaiah Chamakuri
Numerical Methods for Partial Differential Equations
Mathematical Biology Tumor Growth
article

A Posteriori Error‐Driven Space‐Time Adaptive FEM for Accurate Avascular Tumor and Drug Interaction Modeling

Vivek S. Yadav, Gobinda Garai, Nishant Ranwan, Nagaiah Chamakuri
article en

Abstract

ABSTRACT This study presents a space‐time adaptive finite element method (FEM) integrated with a posteriori error analysis to simulate avascular tumor growth and drug interactions in prostate cancer. The model is based on a nonlinear, coupled system of reaction‐diffusion equations, incorporating discontinuities in time to represent drug interactions. We perform a residual‐based a posteriori error analysis, providing precise error estimates for both spatial and temporal components. The approach numerically ensures boundedness and positivity for the phase variable, nutrient, and prostate‐specific antigen (PSA) concentrations. Furthermore, we establish and prove the reliable error estimators for space and time discretization. Numerical tests, including cases with and without drug interactions, show that the estimators accurately capture tumor growth. A comparison of solution convergence using uniform grids and Adaptive Mesh Refinement (AMR) demonstrates the superior efficiency of AMR, significantly reducing computational effort in the absence of drug effects and achieving a substantial reduction in computational costs when drug effects are present. This highlights the effectiveness of AMR in optimizing both accuracy and computational efficiency, making it an essential tool for modeling such complex systems.

Numerical Methods for Partial Differential EquationsVol. 42(2)
Centre for Development of Advanced Computing (IN), University of British Columbia (CA), Indian Institute of Science Education and Research Thiruvananthapuram (IN)
Good health and well-being
Openalex Percentile: Top 36%
Mathematical Biology Tumor Growth
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.