Analysis by Synthesis

Analysis by synthesis (AbS) is a computational approach that infers properties of a system by generating and comparing synthetic outputs to observed data. Current research focuses on improving AbS efficiency and robustness across diverse applications, employing techniques like differentiable rendering, generative transformer models, and reinforcement learning with graph neural networks to synthesize realistic outputs in areas such as audio, 3D shape modeling, and chemical process design. These advancements are enhancing the accuracy and generalizability of AbS methods, leading to improved performance in tasks ranging from speech restoration and 3D object perception to automated controller synthesis for autonomous systems. The broader impact lies in enabling more robust and data-efficient solutions for complex inverse problems across various scientific and engineering domains.