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Showing 1–2 of 2 results
Advanced filters: Author: Rushil Anirudh Clear advanced filters

  • The success of machine learning for scientific discovery normally depends on how well the inherent assumptions match the problem in hand. Here, Thiagarajan et al. alleviate this constraint by allowing the change of optimization criterion in a data-driven approach to emulate complex scientific processes.

    • Jayaraman J. Thiagarajan
    • Bindya Venkatesh
    • Brian Spears
    ResearchOpen Access
    Nature Communications
    Volume: 11, P: 1-10

  • High energy density physics (HEDP) is crucial for advancements in fusion energy and astrophysics, yet its simulations are complex and computationally demanding. The authors introduce HEDP-Gen, a deep learning framework which uses advanced geometry in model design, and show that it enhances simulation efficiency and produces scientifically accurate results.

    • Ankita Shukla
    • Yamen Mubarka
    • Jayaraman J. Thiagarajan
    ResearchOpen Access
    Communications Physics
    Volume: 8, P: 1-11