Sandeep Madireddy
Sandeep Madireddy
Mathematics and Computer Science Division, Argonne National Laboratory
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Cited by
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A Bayesian approach to selecting hyperelastic constitutive models of soft tissue
S Madireddy, B Sista, K Vemaganti
Computer Methods in Applied Mechanics and Engineering 291, 102-122, 2015
Time-series learning of latent-space dynamics for reduced-order model closure
R Maulik, A Mohan, B Lusch, S Madireddy, P Balaprakash, D Livescu
Physica D: Nonlinear Phenomena 405, 132368, 2020
Bayesian calibration of hyperelastic constitutive models of soft tissue
S Madireddy, B Sista, K Vemaganti
Journal of the Mechanical Behavior of Biomedical Materials, Accepted for …, 2016
Machine learning based parallel I/O predictive modeling: A case study on Lustre file systems
S Madireddy, P Balaprakash, P Carns, R Latham, R Ross, S Snyder, ...
International Conference on High Performance Computing, 184-204, 2018
Analysis and correlation of application I/O performance and system-wide I/O activity
S Madireddy, P Balaprakash, P Carns, R Latham, R Ross, S Snyder, ...
2017 International Conference on Networking, Architecture, and Storage (NAS …, 2017
Modeling i/o performance variability using conditional variational autoencoders
S Madireddy, P Balaprakash, P Carns, R Latham, R Ross, S Snyder, ...
2018 IEEE International Conference on Cluster Computing (CLUSTER), 109-113, 2018
Uncertainty quantification using the nearest neighbor Gaussian process
H Shi, EL Kang, BA Konomi, K Vemaganti, S Madireddy
New Advances in Statistics and Data Science, 89-107, 2017
A Modular Deep Learning Pipeline for Galaxy-Scale Strong Gravitational Lens Detection and Modeling
S Madireddy, N Li, N Ramachandra, P Balaprakash, S Habib, J Butler, ...
arXiv preprint arXiv:1911.03867, 2020
Using recurrent neural networks for nonlinear component computation in advection-dominated reduced-order models
R Maulik, V Rao, S Madireddy, B Lusch, P Balaprakash
arXiv preprint arXiv:1909.09144, 2019
Calibration of hyperelastic constitutive models: the role of boundary conditions, search algorithms, and experimental variability
K Kenja, S Madireddy, K Vemaganti
Biomechanics and Modeling in Mechanobiology, 1-18, 2020
Phase Segmentation in Atom-Probe Tomography Using Deep Learning-Based Edge Detection
S Madireddy, DW Chung, T Loeffler, SKRS Sankaranarayanan, ...
Scientific reports 9 (1), 1-10, 2019
Improving Scalability of Parallel CNN Training by Adjusting Mini-Batch Size at Run-Time
S Lee, Q Kang, S Madireddy, P Balaprakash, A Agrawal, A Choudhary, ...
2019 IEEE International Conference on Big Data (Big Data), 830-839, 2019
Multilayer Neuromodulated Architectures for Memory-Constrained Online Continual Learning
S Madireddy, A Yanguas-Gil, P Balaprakash
arXiv preprint arXiv:2007.08159, 2020
Value-Added Chemical Discovery Using Reinforcement Learning
P Jiang, H Doan, S Madireddy, RS Assary, P Balaprakash
arXiv preprint arXiv:1911.07630, 2019
Machine learning of sequential data for non-intrusive reduced-order models
R Maulik, A Mohan, S Madireddy, B Lusch, P Balaprakash, D Livescu
APS, H10. 003, 2019
Adaptive Learning for Concept Drift in Application Performance Modeling
S Madireddy, P Balaprakash, P Carns, R Latham, GK Lockwood, R Ross, ...
Proceedings of the 48th International Conference on Parallel Processing, 1-11, 2019
Neuromorphic Architecture Optimization for Task-Specific Dynamic Learning
S Madireddy, A Yanguas-Gil, P Balaprakash
Proceedings of the International Conference on Neuromorphic Systems, 1-5, 2019
On the Inference of Viscoelastic Constants from Stress Relaxation Experiments
K Vemaganti, S Madireddy, S Kedari
Mechanics of Time-Dependent Materials, 2019
In Situ Compression Artifact Removal in Scientific Data Using Deep Transfer Learning
S Madireddy, JH Park, S Lee, P Balaprakash, S Yoo, W Liao, C Hauck, ...
Preprint, ANL/MCS-P9162-0219, 2019
New Advances in Statistics and Data Sceince
S Madireddy, B Konomi, E Kang, H Shi, M Tuomisto, J Mottonen, T Nummi, ...
Springer International Publishing, 2017
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