Research Results


D. Ghoshal, L. Ramakrishnan and D. Agarwal. “Dac-Man: Data Change Management for Scientific Datasets on HPC Systems." In the International Conference for High Performance Computing, Networking, Storage and Analysis (SC’18). 2018. [pdf] [SC18-Talk]

B.Faybishenko, F. Molz, D.Agarwal, Nonlinear Dynamics Simulations of Ecological Processes: Model, Diagnostic Parameters of Deterministic Chaos, and Sensitivity Analysis. Chapter (invited) in book "Stochastic Processes and Algebraic Structures–From Theory Towards Applications" (Springer, in Press), 2018.  

Molz F, Faybishenko B, Agarwal D (2018) A broad exploration of nonlinear dynamics in microbial systems motivated by chemostat experiments producing deterministic chaos. LBNL-2001172, Berkeley, CA.

William Fox, Devarshi Ghoshal, Abel Souza, Gonzalo P. Rodrigo, Lavanya Ramakrishnan, E-HPC: A Library for Elastic Resource Management in HPC Environments, Workflows in Support of Large-Scale Science (WORKS), workshop held at Supercomputing. 2017. [pdf]

Faybishenko, B., Detecting dynamic causal inference in nonlinear two-phase fracture flow, Advances in Water Resources 106 (2017), 111–120.


Arora, B., B. Faybishenko, and D. Agarwal (2018), Using Sensitivity Analysis as a Tool to Determine the Need for Regeneration of Hydrological and Biogeochemical Predictions, Submitted Abstract, AGU Fall Meeting, Washington D. C., 10-14 December.

Powell,T., B. Faybishenko, D.Agarwal and L.M. Kueppers, Amendments in local meteorological data alter tropical forest biomass predictions of a terrestrial ecosystem model. Submitted abstract to Fall 2018 AGU Annual Meeting.


D. Ghoshal and others, Deduce: Managing Data Change Pipelines, Poster. Sep 2017. [pdf]

J. Mueller and others, Deduce: Understanding the Types and Impact of Data Change, Poster. Sep 2017. [pdf]

Faybishenko, B., T.Tokunaga, Y.Kim, D.Agarwal, Uncertainty Propagation in Predictions of Hydraulic Parameters Based on Experimental data and Pedotransfer Functions. Fall 2016 AGU, Poster presentation B13E0668.