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Senast publicerade

Long–time behaviour of Hamiltonian PDEs driven by an additive one dimensional standard Brownian motion and their numerical discretisations
(2026) Mowed, Homam; Abonaser, Sandy
This thesis investigates the long–time behaviour of Hamiltonian partial differential equations driven by an additive one dimensional standard Brownain motion. The study aims to understand how random fluctuations affect fundamental properties, mostly in classical systems, analytically and numerically. Deterministic Hamiltonian ODEs and PDEs are first examined, after which stochastic perturbations are introduced and analyzed using stochastic differential equations, Itô calculus and numerical methods. A numerical integrator called Strang–AVF is derived, which preserves the long–time behaviour of the expected value of the Hamiltonian in stochastic systems.
Exploring Tumor Evolution in Pediatric CNS Tumors: Evaluation of EVOFLUX-Derived Parameters and Posterior Summary Statistics from DNA Methylation Data
(2026) Irenheim Jarlhed, My
In this thesis, the EVOFLUX framework was investigated as a method for inferring evolutionary features from DNA methylation data. The DNA methylation data was taken from pediatric central nervous systemCNS tumor samples. EVOFLUX was originally developed for lymphoid malignancies and uses fluctuating CpG sites (fCpGs) to estimate parameters describing tumor evolution. The framework was applied to pediatric CNS tumor samples from Barntumörbanken. The fCpG selection and filtering procedure was evaluated for the tumor samples, after which EVOFLUX was used to infer parameters including tumor growth rate, tumor age, methylation switching rates, and effective population size. The resulting posterior distributions were summarized using the median, median absolute deviation (MAD), skewness, and kurtosis. The inferred parameters and summary statistics were compared between age groups and tumor types and were evaluated in relation to time to first relapse. The analyses showed that the age of the patients were associated with differences of the inferred parameters. And that this is needed to take into account when doing analyses. Summary statistics describing the shape of the posterior distributions were evaluated and showed to also have importance meaning that using only the median of the posterior distributions would leave out possibly important information. The results indicate that EVOFLUX can be used for analysis of evolutionary features in pediatric CNS tumor methylation data and that statistics describing shape of the posterior distributions can provide further insights. However, the survival analyses were limited by small sample sizes and median-based cutoffs. Larger cohorts and additional clinical data are needed to validate these findings and determine whether EVOFLUX-derived features can be useful for relapse prediction.
Integrating a Neuro-Symbolic Planner for Safe Driving
(2026) Alvandi, Saghar; Wingård, Jennifer
Ensuring the safety and reliability of autonomous driving systems remains an ongoing challenge. The complexity of real-world traffic scenarios highlights the importance of autonomous driving systems being able to reason about their surrounding environments and make safe decisions. This thesis proposes a modular neuro-symbolic framework that decouples trajectory prediction from motion planning. Rather than relying solely on an end-to-end deep-learning-based planner, the proposed approach uses a fine-tuned large language model to predict the future trajectories of surrounding actors. These predictions are incorporated as dynamic obstacles into a symbolic A* planner, which generates a goal-oriented trajectory for the ego vehicle subject to safety constraints. The proposed framework is evaluated on data generated from driving scenarios developed in Scenic and executed in the CARLA simulator, with its performance compared against an end-to-end deep-learning-based planner using the same set of 144 scenarios. The results demonstrate that this modular neuro-symbolic approach achieves a higher rate of safe, goal-reaching trajectories, with a success rate of 59.0% compared with 27.1% for the baseline planner. However, the neuro-symbolic planner also exhibits a higher collision rate of 32.6%, compared with 10.4% for the baseline planner. This difference is partly due to the baseline frequently producing safe but non-goal-reaching trajectories, with 57 scenarios falling into this category because the ego vehicle remains at least 100 meters away from the other actors throughout the scenario. These findings highlight a trade-off between collision avoidance and goal achievement, indicating the need for further evaluation to draw clearer conclusions regarding the safety of the approach.
Structure-borne noise assessment of automotive componentsusing theblocked force approach
(2026) Man, Nicholas
Changes in the vehicle industry have heightened the demand for performing acoustic and vibration analysis earlier in the product development process. This is challenging as NVH issues are often identified during full system integration, when design changes are costly and limited due to frozen vehicle configurations. To address this, virtual prototypes can be developed where components are independently characterized at their interfaces and transferred between assemblies. A major limitation of this approach is that vibration forces from source components are not inherently independent, as interface forces obtained through conventional methods depend on boundary conditions and are therefore not transferable between systems. To over come this, the in-situ blocked force method has been introduced to characterize source components such that the resulting interface forces are intrinsic to the source and independent of the receiving structure. This thesis presents a case study applying the in-situ blocked force method to an electro-hydraulic steering pump as a source component. The objectives are to validate the in-situ blocked force method, demonstrate that the derived blocked forces are independent of the receiving structure and transferable between systems, and establish a workflow for digital twin based acoustic and vibration analysis. The results show good consistency of the derived blocked forces across two dynamically di!erent receiving structures. This consistency is confirmed through cross-validation between test configurations. Furthermore, the developed digital twin enables component-based transfer path analysis and the simulation of modification scenarios
Semantic Communication for Closed-loop Sensing-Communication-Control Systems
(2026) Wang, Jingcheng
This thesis investigates channel state information (CSI) as a sensing input for position estimation and simulated closed-loop control, motivated by the Level-2 interpretation of semantic communication. It evaluates task-relevant state extraction rather than a modeled semantic transmission link. A simulation pipeline combines Sionna RT channel generation in a street-canyon scene, a ResNet-based estimator, Kalman filtering, and linear quadratic regulator (LQR) control. Complex CSI is represented by real and imaginary components across antennas and subcarriers. The neural model predicts two-dimensional position and coordinate-wise variance. Position predictions supply measurements to the filter, while the controller acts on the filtered state. The learned covariance is evaluated separately; the main closed-loop experiments use fixed diagonal covariance. On a random held-out test split of 2000 samples within the simulated scene, the estimator achieves a mean Euclidean localization error of 0.857 m, an RMSE of 1.204 m, and a median error of 0.673 m. Joint antenna–subcarrier masking yields an accuracy plateau: retaining 56.25% of the full CSI coefficients gives the lowest observed mean error, 0.626 m. This does not establish bitrate or energy savings. Predicted covariance is weakly correlated with localization errors and fails the reported consistency check, limiting its direct use in Kalman filtering. The fixed-covariance sweep demonstrates tracking sensitivity to measurement confidence. In one paired seed-0 rollout per setting, a variance of 1 m2 per coordinate yields the lowest true-state LQR cost, 750.27, and tracking RMSE, 0.683 m, among the tested settings. These findings characterize within-scene localization, uncertainty limitations, and covariance-sensitive tracking. Ideal interfaces, one scene and trajectory, and missing controller controls restrict conclusions about communication benefits, generalization, and the contribution of individual pipeline components. Calibrated uncertainty and broader evaluation are needed before deployment.