Energy Deposition

Energy deposition research focuses on understanding and controlling how energy is transferred and absorbed within materials, primarily within the context of additive manufacturing and particle physics. Current research emphasizes the development of accurate and computationally efficient predictive models, leveraging machine learning techniques like deep neural networks (including convolutional and recurrent architectures), Gaussian processes, and physics-informed neural networks, often combined with Bayesian optimization for process optimization. These advancements are crucial for improving the precision and efficiency of additive manufacturing processes, enabling the creation of complex materials with tailored properties, and for accelerating simulations in high-energy physics experiments.