Singularity Free

"Singularity-free" research addresses challenges arising from singularities—points where mathematical models or algorithms become ill-defined or unstable—across diverse fields. Current efforts focus on developing methods to avoid or mitigate these singularities, employing techniques like switched ODEs in generative models, minimum-jerk approaches in robotics, and novel information criteria in machine learning model selection. Overcoming these singularities improves the robustness and efficiency of various applications, ranging from robotic control and image generation to the training of deep neural networks and the analysis of complex geometric shapes.