AI and Machine Learning

Real-Time Laser Performance Modeling

LLE uses advanced models to predict how high-power lasers behave – capturing their energy output, pulse shapes, and beam profiles. Future lasers for fusion energy will need to operate at much higher repetition rates, on the order of 10 shots per second, which demands real-time prediction and control of laser parameters. Our researchers have shown that data-driven methods, such as deep learning, can learn from experimental data and capture important effects that are difficult or impractical to include in purely physics-based simulations. By integrating physics-based understanding with these data-driven models, we aim to create accurate, real-time predictive tools that will enable the reliable, high-speed operation required to make fusion a viable energy source.

AI-Driven Optimization for Direct-Drive ICF Implosions

Fusion requires compressing tiny fuel capsules with powerful lasers until they reach extreme pressures and temperatures. A major challenge is keeping the implosion perfectly symmetrical; even small imperfections waste energy and reduce performance. LLE researchers developed machine-learning tools that turn x-ray images into 3D reconstructions of the imploding capsule, analyze sources of asymmetry, and suggest laser adjustments in real time. These advances have already improved experimental results, bringing fusion energy research closer to practical success.

Bayesian Optimization to Accelerate Fusion Research

Inertial confinement fusion (ICF) is a promising approach to achieving fusion energy by using powerful lasers to compress tiny fuel capsules until they reach the extreme conditions needed for fusion reactions.  However, the parameter space for laser pulse shapes and target designs is extremely large, making manual or brute-force searches impractical. To overcome this, LLE researchers have developed an automated and highly parallelized Bayesian optimization framework that efficiently explores design possibilities while incorporating experimental data and simulations. Applied on the Omega Laser Facility, this approach produced designs predicted to approach ignition conditions, demonstrating the potential of machine learning to accelerate fusion research.

Fast and Accurate Equation-of-State Modeling

Artificial intelligence has been used to improve models that describe how matter behaves under extreme pressure and temperature, such as in fusion experiments or inside planets. Traditional physics-based simulations are very accurate but take enormous time and computer power, and the data from experiments are often scarce. In collaboration with the Rochester Institute of Technology, we have created a new machine-learning model that predicts both pressure and energy while still respecting the laws of thermodynamics. This approach makes reliable predictions even when data are limited, can recognize when results are uncertain, and runs much faster than traditional methods-helping advance fusion and planetary science.

Simulating Fusion Fuel with Quantum Accuracy at Supercomputer Speeds

Deuterium, a hydrogen isotope, is central to both fusion energy and planetary science. When subjected to powerful shocks, it undergoes a transition from a molecular fluid to an atomic, metallic state, but capturing this process with high accuracy is extremely difficult. Quantum mechanical simulations can describe it precisely but are too computationally expensive for large systems, while simpler models miss essential physics. One area of research at LLE focuses on building advanced models of how atoms interact under extreme conditions. These models, called interatomic potentials, describe the forces between atoms and are essential for running large-scale computer simulations of materials. Normally, these potentials are either simple formulas (fast but not very accurate) or derived directly from quantum mechanics (very accurate but far too slow for large systems). The machine learning approach bridges this gap. By training on data from quantum-level simulations of deuterium, the model “learns” the underlying physics of atomic interactions. Once trained, the potential can reproduce quantum accuracy while being efficient enough to run large-scale simulations of millions of atoms under shock conditions.

Angularly Resolved Thomson Scattering Analysis

The Angularly Resolved Thomson Scattering (ARTS) diagnostic was invented at LLE to measure the distribution of electron velocities in the plasma. This pioneering diagnostic uses machine learning implemented on advanced GPU technologies to extract a two dimensional electron distribution function from the measured ARTS spectrum.

These measured distribution functions will ultimately be used to study how energy is transported in a plasma along a temperate gradient produced on the Omega Laser Facility.