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

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