5 papers
Using Echo-State Networks to Reproduce Rare Events in Chaotic Systems
Anton Erofeev, Balasubramanya T. Nadiga, Ilya Timofeyev
We apply Echo-State Networks to predict time series and statistical properties of the competitive Lotka-Volterra model in the chaotic regime. In particular, we demonstrate that Ech…
Quantum Homotopy Algorithm for Solving Nonlinear PDEs and Flow Problems
Sachin S. Bharadwaj, Balasubramanya Nadiga, Stephan Eidenbenz +1
Quantum algorithms to integrate nonlinear PDEs governing flow problems are challenging to discover but critical to enhancing the practical usefulness of quantum computing. We prese…
Physics consistent machine learning framework for inverse modeling with applications to ICF capsule implosions
Daniel A. Serino, Evan Bell, Marc Klasky +4
In high energy density physics (HEDP) and inertial confinement fusion (ICF), predictive modeling is complicated by uncertainty in parameters that characterize various aspects of th…
Learning Robust Features for Scatter Removal and Reconstruction in Dynamic ICF X-Ray Tomography
Siddhant Gautam, Marc L. Klasky, Balasubramanya T. Nadiga +3
Density reconstruction from X-ray projections is an important problem in radiography with key applications in scientific and industrial X-ray computed tomography (CT). Often, such…
Limitations for Quantum Algorithms to Solve Turbulent and Chaotic Systems
Dylan Lewis, Stephan Eidenbenz, Balasubramanya Nadiga +1
We investigate the limitations of quantum computers for solving nonlinear dynamical systems. In particular, we tighten the worst-case bounds of the quantum Carleman linearisation (…