Dynamic Parameter Estimation Utilizing Optimized Trajectories. - Argtim Tika; Jonas Ulmen; Naim Bajçinca (IROS, 2020)

Development of an End-to-End Tool Chain for AI-Assisted Generation and Optimization of Operating Strategies for Hybrid Electric Vehicles. - Tobias Gehra; Ganesh Sundaram; Mirjan Heubaum; Jonas Ulmen; Michael Günthner; Daniel Görges (ICECET, 2023)

Stochastic MPC with Situation-Aware Dynamic Risk Assessment for Autonomous Driving. - Jonas Ulmen; Marc Wellstein; Christoph Mark; Ganesh Sundaram; Daniel Görges; Jan Reich (ITSC, 2023)

State Derivative Normalization for Continuous-Time Deep Neural Networks. - Jonas Weigand; Gerben Izaak Beintema; Jonas Ulmen; Daniel Görges; Roland Tóth; Maarten Schoukens; Martin Ruskowski (CoRR, 2024)

Enhanced Pruning Strategy for Multi-Component Neural Architectures Using Component-Aware Graph Analysis. - Ganesh Sundaram; Jonas Ulmen; Daniel Görges (CoRR, 2025)

Application-Specific Component-Aware Structured Pruning of Deep Neural Networks via Soft Coefficient Optimization. - Ganesh Sundaram; Jonas Ulmen; Amjad Haider; Daniel Görges (CoRR, 2025)

COMponent-Aware Pruning for Accelerated Control Tasks in Latent Space Models. - Ganesh Sundaram; Jonas Ulmen; Amjad Haider; Daniel Görges (CoRR, 2025)

Learning State-Space Models of Dynamic Systems from Arbitrary Data using Joint Embedding Predictive Architectures. - Jonas Ulmen; Ganesh Sundaram; Daniel Görges (CoRR, 2025)