Learning physics-based models from data: perspectives from inverse problems and model reduction

O Ghattas, K Willcox - Acta Numerica, 2021‏ - cambridge.org
This article addresses the inference of physics models from data, from the perspectives of
inverse problems and model reduction. These fields develop formulations that integrate data …

Integrated biophysical modeling and image analysis: application to neuro-oncology

A Mang, S Bakas, S Subramanian… - Annual review of …, 2020‏ - annualreviews.org
Central nervous system (CNS) tumors come with vastly heterogeneous histologic,
molecular, and radiographic landscapes, rendering their precise characterization …

Predictive digital twin for optimizing patient-specific radiotherapy regimens under uncertainty in high-grade gliomas

A Chaudhuri, G Pash, DA Hormuth… - Frontiers in Artificial …, 2023‏ - frontiersin.org
We develop a methodology to create data-driven predictive digital twins for optimal risk-
aware clinical decision-making. We illustrate the methodology as an enabler for an …

TGM-Nets: A deep learning framework for enhanced forecasting of tumor growth by integrating imaging and modeling

Q Chen, Q Ye, W Zhang, H Li, X Zheng - Engineering Applications of …, 2023‏ - Elsevier
Prediction and uncertainty quantification of tumor progression are vital in clinical practice, ie,
disease prognosis and decision-making on treatment strategies. In this work, we propose …

Modelling glioma progression, mass effect and intracranial pressure in patient anatomy

J Lipková, B Menze, B Wiestler… - Journal of the …, 2022‏ - royalsocietypublishing.org
Increased intracranial pressure is the source of most critical symptoms in patients with
glioma, and often the main cause of death. Clinical interventions could benefit from non …

A fast and scalable computational framework for large-scale high-dimensional Bayesian optimal experimental design

K Wu, P Chen, O Ghattas - SIAM/ASA Journal on Uncertainty Quantification, 2023‏ - SIAM
We develop a fast and scalable computational framework to solve Bayesian optimal
experimental design problems governed by partial differential equations (PDEs) with …

A deep neural network for operator learning enhanced by attention and gating mechanisms for long-time forecasting of tumor growth

Q Chen, H Li, X Zheng - Engineering with Computers, 2024‏ - Springer
Forecasting tumor progression and assessing the uncertainty of predictions play a crucial
role in clinical settings, especially for determining disease outlook and making informed …

Quantitative in vivo imaging to enable tumour forecasting and treatment optimization

G Lorenzo, DA Hormuth II, AM Jarrett… - Cancer, complexity …, 2022‏ - Springer
Current clinical decision-making in oncology relies on averages of large patient populations
to both assess tumour status and treatment outcomes. However, cancers exhibit an inherent …

Learn-Morph-Infer: a new way of solving the inverse problem for brain tumor modeling

I Ezhov, K Scibilia, K Franitza, F Steinbauer, S Shit… - Medical Image …, 2023‏ - Elsevier
Current treatment planning of patients diagnosed with a brain tumor, such as glioma, could
significantly benefit by accessing the spatial distribution of tumor cell concentration. Existing …

An offline-online decomposition method for efficient linear Bayesian goal-oriented optimal experimental design: Application to optimal sensor placement

K Wu, P Chen, O Ghattas - SIAM Journal on Scientific Computing, 2023‏ - SIAM
Bayesian optimal experimental design (OED) plays an important role in minimizing model
uncertainty with limited experimental data in a Bayesian framework. In many applications …