Reduced-order, behavioral, surrogate and stochastic modeling


In practically any field of engineering, the complexity of modern systems often prevents a direct numerical simulation of an entire product, such as a high-performance server, an automobile, or an aircraft. Reduced-complexity models are essential to represent dominant physical effects and dynamics, neglecting those phenomena that are unimportant. Reduced-order models or behavioral models can be derived for devices, components, subsystems or even entire systems including multi physics effects. Models can further be parameter-dependent to enable surrogate-based design and fast optimization. When parameters are uncertain, models can be stochastic, in order to predict not only signals and responses but also their variations, sensitivities, probability distributions and percentiles.

The systematic derivation of the above compact models is the main subject of this research activity, which investigates both model-driven and data-driven techniques. Depending on the particular application at hand, fully deterministic models or variation-aware/stochastic settings are considered. Applications include Electromagnetic Compatibility, Signal and Power Integrity of electrical and electronic systems, but also any type of physical system, including multi-domain and multi-physics.

ERC sectors 

  • PE7_2 Electrical engineering: power components and/or systems
  • PE7_3 Simulation engineering and modelling
  • PE7_4 (Micro- and nano-) systems engineering
  • PE7_5 (Micro- and nano-) electronic, optoelectronic and photonic components
  • PE7_12 Electrical energy production, distribution, applications
  • PE8_4 Computational engineering
  • PE6_11 Machine learning, statistical data processing and applications using signal processing (e.g. speech, image, video)
  • PE6_12 Scientific computing, simulation and modelling tools


  • Reduced-order modeling
  • Vector Fitting
  • Parameterized macromodeling
  • Surrogate modeling
  • Linear and nonlinear dynamical systems
  • Statistical analysis
  • Uncertainty quantification
  • Machine learning
  • Complex systems