Lawrence Livermore National Laboratory



Brian Spears

Executive Summary

We are employing machine learning techniques to develop more accurate predictive models that are trained jointly on both simulation and experimental data, comparing more forms of data than is typical, such as images, vector-valued data, and scalars. This research supports the national energy and security missions that rely on predictive models.

Publications, Presentations, Etc.

Castillo, V. 2019. "Enhancing Experimental Design and Understanding with Deep Learning/AI." LLNL-PRES-748201.

——— . 2019. "Machine Learning for Better Understanding and Control of Complex Processes." LLNL-PRES-769927.

Castillo, V. and Y. Mubarka. 2019. "Enhancing Experimental Design with A0.I." LLNL-PRES-759174.

Castillo, V., et al. 2019. "Asynchronous Method for Active Learning on HPC for Efficient Exploration of Complex Systems." LLNL-POST-754786.

——— . 2019. "Developing Fast-running Simulations Models for Manufacturing Using Deep Learning." LLNL-PRES-769382.

Jacobs, S. 2019. "LTFB: Training Neural Networks at Sierra Scale." GPU Technology Conference. LLNL-ABS-774862.

Jacobs, S., et al. 2019. "Parallelizing Training of Deep Generative Models on Massive Scientific Datasets." The IEEE Cluster Conference. doi:10.1109/CLUSTER.2019.8891012. LLNL-CONF-776577.

——— . 2019. "Parallelizing Training of Deep Generative Models on Massive Scientific Datasets." LLNL-PRES-791164.

Koning, J., et al. 2019. "Merlin Workflow: Machine Learning Driven Ensembles." LLNL-PRES-769884.

Kustowski, B. 2019. "Correcting Predictions of a Computer Simulation to Match the Experiments Using Transfer Learning." LLNL ASC Machine Learning Workshop, 2019. LLNL-PRES-790119.

Kustowski, B., et al. 2019. "Correcting Predictions of a Deficient Simulation Code Using Transfer Learning." Research Challenges at the interface of Machine Learning and Uncertainty Quantification, USC, 2019. LLNL-PRES-782122.

——— . 2019. "Early Prediction of the Simulation Outputs." APS DPP 2019. LLNL-PRES-793722.

——— . 2019. "Transfer Learning as a Tool for Reducing Simulation Bias: Application to Inertial Confinement Fusion." IEEE Trans. on Plasma Phys. LLNL-PROC-770955.

——— . 2019. "Transfer Learning for the Calibration of the ICF Simulations." LLNL-POST-755064.

——— . 2019. "Transfer Learning for the Calibration of the Inertial Confinement Fusion Simulations." APS-DPP, 2018. LLNL-PRES-760734.

Peterson, J. 2019. "Aiding Computational Workflows with Machine Learning." LLNL-PRES-745444.

——— . 2019. "Machine Learning Aided Discovery of a New NIF Design." LLNL-PRES-755427.

——— . 2019. "Fail Fast and Often! And Take Down Other People Too!" LLNL-PRES-782657.

——— . 2019. "Learning-Based Predictive Models: A New Approach to Integrating Large-Scale Simulations and Experiments." Machine Learning for Computational Fluid and Solid Dynamics. LLNL-ABS-767192.

Spears, B. 2017. "Ensemble Simulations of Inertial Confinement Fusion Implosions." Stat. Anal. Data Min. 10. 230–237. doi:10.1002/sam.11344. LLNL-JRNL-694784.

——— . 2018. "Deep Learning: A Guide for Practitioners in the Physical Sciences." Phys. Plasmas. 25. doi:10.1063/1.5020791. LLNL-JRNL-743970.

——— . 2019. "Learning-Based Predictive Models: A New Approach to Integrating Large-Scale Simulations and Experiments." LLNL-ABS-758705 and LLNL-PRES-735160.

——— . 2019. "Learning-Based Predictive Models: A New Approach to Integrating Large-Scale Simulations and Experiments." Intl Conf. on Data Driven Plasma Sci. LLNL-PRES-756925.

Van Essen, B. 2019. "Cognitive Simulation: Intersection of Large Scale Machine Learning and Scientific Computing." LLNL-PRES-778998.

——— . 2019. "Learning-Based Predictive Models: A New Approach to Integrating Large-Scale Simulations and Experiments." LLNL-PRES-757656.