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portfolio
Portfolio item number 2
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publications
A Hybrid Conditional Diffusion-DeepONet Framework for High-Fidelity Stress Prediction in Hyperelastic Materials
Published in SSRN, 2024
This paper presents a hybrid framework combining conditional diffusion models and DeepONet for high-fidelity stress prediction.
Recommended citation: Kota, P.V., Rashid, M.M., Goswami, S., Graham-Brady, L. (2024). "A Hybrid Conditional Diffusion-DeepONet Framework for High-Fidelity Stress Prediction in Hyperelastic Materials." Available at SSRN 6448293. https://ssrn.com/abstract=6448293
Uncertainty-Aware Multi-Output Gaussian-Process Autoregression for Melt-Pool Dynamics and Microstructural Implications
Published in SSRN, 2025
This paper explores uncertainty-aware multi-output Gaussian-process autoregression applied to melt-pool dynamics and microstructures.
Recommended citation: Kota, P.V., Biswas, S., German, P., Graham-Brady, L., Dhulipala, S. (2025). "Uncertainty-Aware Multi-Output Gaussian-Process Autoregression for Melt-Pool Dynamics and Microstructural Implications." Available at SSRN 5803975. https://ssrn.com/abstract=5803975
talks
Generative Uncertainty for Predicting Uncertainty in Material Response
Published:
Poster presentation on modeling generative uncertainty for material discovery.
On Combining Neural Operators and Denoising Diffusion Models for Predicting Material Stress Response
Published:
Presented research on integrating neural operators with diffusion models to predict multi-scale material stress responses.
Stress Field Prediction using Denoising Diffusion and Neural Operator Models
Published:
Presentation covering the use of generative AI and deep operator networks for stress field prediction.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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EN.560.201 Statics & Mechanics of Materials
Undergraduate Course, Johns Hopkins University, Department of Civil & Systems Engineering, 2024
Served as a Graduate Teaching Assistant under the advisement of Prof. Rachel Sangree for the Fall 2024 semester.