**Final full program (oral and poster presentations with abstracts): ****Program PDF file**

**List of posters (with supplementary material link when available)**

Poster session A

PA01 – Identification of physiological lung parameters using the forced oscillation technique

PA02 – Dynamic inversion based estimation of COVID-19 epidemiological data

PA03 – Temperature dependent parameter estimation of Li-ion batteries

PA04 – Identification of the low speed steering dynamics of an autonomous car from real and simulation data

PA05 – A simplified frequency domain approach for local module identification in dynamic

PA06 – Identification of nonlinear system linearized around a trajectory by Gaussian process

PA07 – A novel deep neural network architecture for non-linear system identification

PA08 – Shaping multisine excitation for closed-loop identification of a mechanical transmission

PA09 – Willems’ fundamental lemma based on second-order moments

PA10 – Uncertainty quantification of input matrix and transfer function estimates in subspace identification

PA11 – A layer potential approach to functional and clinical brain imaging

PA12 – Model Validation of Mostar Hydroelectric Plant Using PMU and Synthetic Data

PA13 – Identification of Non-linear Differential-Algebraic Equations Disturbed by Stochastic Processes

PA14 – Parameter Estimation of Parallel Wiener-Hammerstein Systems by Decoupling their Volterra Representations

**Poster session B**

PB01 – Identifying Faults – A Closed-loop Perspective

PB02 – A scalable multi-step least squares method for network identification with unknown disturbance topology

PB03 – Dynamical system identification from video data using subspace encoders

PB04 – Accurate H∞-norm estimation via finite-frequency norms of local parametric models

PB05 – Non-causal regularized least-squares for continuous-time system identification with band-limited input excitations

PB06 – Control and Estimation of Ensembles via Structured Optimal Transport

PB07 – Dual adaptive model predictive control using application-oriented set membership identification

PB08 – Necessary Graph Condition for Local Network Identifiability

PB09 – Distance correlation screening for separable decompositions of MIMO non-linear systems

PB10 – Identification of nonlinear systems using LPV model identification around a time-varying trajectory

PB11 – Regularized switched system identification: a statistical learning perspective

PB12 – Excitation allocation for generic identifiability of linear dynamic networks with fixed modules

PB13 – Improved Experiment Design for the Identification of Complex Real World Systems

PB14 – Long-term individual household electrical consumption forecasting

**Poster session C**

PC01 – Local identification in physical networks

PC02 – Bayesian tensor network-based Volterra system identification

PC03 – Large Scale Learning With Fourier Features And Tensor Decompositions

PC04 – Decoupling multivariate functions using a nonparametric filtered tensor decomposition

PC05 – What is the Koopman form of nonlinear systems with inputs?

PC06 – Correctional Learing via Cooperative System Identification

PC07 – Signal Matrix Model in Simulation, Signal Denoising and Control Design

PC08 – Nonlinear model estimation via linearization around

PC09 – Multi-armed bandit schemes for adaptive model predictive control

PC10 – Incorporating prior knowledge in kernel-based estimators: a frequency domain approach

PC11 – Koopman operators for Reinforcement Learning

PC12 – Robust-control-relevant experiment design and system identification

PC13 – A recursive algorithm to compute a numerical basis of the null space of the block Macaulay matrix

PC14 – Length of stay prediction in a simulated hospital environment using transfer learning