Anirban Chandra

Anirban Chandra, Marius Koch, Suraj Pawar et al.

This study aims to develop surrogate models for accelerating decision making processes associated with carbon capture and storage (CCS) technologies. Selection of sub-surface $CO_2$ storage sites often necessitates expensive and involved simulations of $CO_2$ flow fields. Here, we develop a Fourier Neural Operator (FNO) based model for real-time, high-resolution simulation of $CO_2$ plume migration. The model is trained on a comprehensive dataset generated from realistic subsurface parameters and offers $O(10^5)$ computational acceleration with minimal sacrifice in prediction accuracy. We also explore super-resolution experiments to improve the computational cost of training the FNO based models. Additionally, we present various strategies for improving the reliability of predictions from the model, which is crucial while assessing actual geological sites. This novel framework, based on NVIDIA's Modulus library, will allow rapid screening of sites for CCS. The discussed workflows and strategies can be applied to other energy solutions like geothermal reservoir modeling and hydrogen storage. Our work scales scientific machine learning models to realistic 3D systems that are more consistent with real-life subsurface aquifers/reservoirs, paving the way for next-generation digital twins for subsurface CCS applications.

Dipayan Sanpui, Anirban Chandra, Henry Chan et al.

We present a probabilistic framework to accurately estimate dimensions of additively manufactured components. Using a dataset of 405 parts from nine production runs involving two machines, three polymer materials, and two-part configurations, we examine five key design features. To capture both design information and manufacturing variability, we employ models integrating continuous and categorical factors. For predicting Difference from Target (DFT) values, we test deterministic and probabilistic machine learning methods. Deterministic models, trained on 80% of the dataset, provide precise point estimates, with Support Vector Regression (SVR) achieving accuracy close to process repeatability. To address systematic deviations, we adopt Gaussian Process Regression (GPR) and Bayesian Neural Networks (BNNs). GPR delivers strong predictive performance and interpretability, while BNNs capture both aleatoric and epistemic uncertainties. We investigate two BNN approaches: one balancing accuracy and uncertainty capture, and another offering richer uncertainty decomposition but with lower dimensional accuracy. Our results underscore the importance of quantifying epistemic uncertainty for robust decision-making, risk assessment, and model improvement. We discuss trade-offs between GPR and BNNs in terms of predictive power, interpretability, and computational efficiency, noting that model choice depends on analytical needs. By combining deterministic precision with probabilistic uncertainty quantification, our study provides a rigorous foundation for uncertainty-aware predictive modeling in AM. This approach not only enhances dimensional accuracy but also supports reliable, risk-informed design strategies, thereby advancing data-driven manufacturing methodologies.