Continuous-depth learning has recently emerged as a novel perspective on deep learning, improving performance in tasks related to dynamical systems and density estimation. Core to these approaches is the neural differential equation, whose forward passes are the solutions of an initial value problem parametrized by a neural network. Unlocking the full potential of continuous-depth models requires a different set of software tools, due to peculiar differences compared to standard discrete neural networks, e.g inference must be carried out via numerical solvers. We introduce TorchDyn, a PyTorch library dedicated to continuous-depth learning, designed to elevate neural differential equations to be as accessible as regular plug-and-play deep learning primitives. This objective is achieved by identifying and subdividing different variants into common essential components, which can be combined and freely repurposed to obtain complex compositional architectures. TorchDyn further offers step-by-step tutorials and benchmarks designed to guide researchers and contributors.
EqOD: Symmetry-Informed Stability Selection for PDE Identification
Axiom Research Group（Axiom研究组）；Department of Applied Mathematics and Computational Science, NM-AIST, Tanzania（应用数学与计算科学系，NM-AIST，坦桑尼亚）；African Institute for Mathematical Sciences (AIMS), Research and Innovation Centre, Rwanda（非洲数学科学研究所（AIMS），研究与创新中心，卢旺达）；The University of Tokyo, Japan（东京大学，日本）
Active Disruption Avoidance and Trajectory Design for Tokamak Ramp-downs with Neural Differential Equations and Reinforcement Learning
Plasma Science and Fusion Center, Massachusetts Institute of Technology（麻省理工学院等离子体科学与聚变中心）；Laboratory for Information and Decision Systems, Massachusetts Institute of Technology（麻省理工学院信息与决策系统实验室）；OpenStar Technologies LLC.（OpenStar科技有限责任公司）
CompNO: A Novel Foundation Model approach for solving Partial Differential Equations
Mines Paris - PSL University, Centre for Material Forming (CEMEF)（巴黎 Mines - PSL 大学，材料成型中心（CEMEF））；Mines Paris - PSL University, Centre for Robotics (CAOR)（巴黎 Mines - PSL 大学，机器人中心（CAOR））
DLGA-PDE: Discovery of PDEs with incomplete candidate library via combination of deep learning and genetic algorithm
College of Engineering, Peking University（北京大学工学院）；School of Environmental Science and Engineering, Southern University of Science and Technology（南方科技大学环境科学与工程学院）；Peng Cheng Laboratory（鹏城实验室）
SODAs: Sparse Optimization for the Discovery of Differential and Algebraic Equations
Department of Engineering Sciences and Applied Mathematics, Northwestern University（工程科学与应用数学系，西北大学）；Department of Physics and Astronomy, Northwestern University（物理与天文学系，西北大学）
APOD: reasoning-guided agentic population ordinary differential equation discovery for pharmacological digital twins
University Côte d’Azur（蔚蓝海岸大学）；Cancer Research Center of Marseille（马赛癌症研究中心）；Institut Paoli-Calmettes（保利-卡尔梅特研究所）；Mines Paris（巴黎矿业学院）；PSL University（巴黎文理研究大学）；Université Paris-Saclay（巴黎萨克雷大学）；CNRS（法国国家科学研究中心）；Institut Galien Paris-Saclay（巴黎萨克雷加利安研究所）；University of Cambridge（剑桥大学）
