20 citations · 67 across the 24 of their papers we have counts for
8 papers · 1 filter
On the Compatibility of DEIM Subspaces for Nonlinear Forces Reduction in Projective Dynamics
Shaimaa Monem Abdelhafez, Igor Pontes Duff, Peter Benner
In physics-based simulations, the evaluation of nonlinear constraint forces represents a primary source of computational complexity. Within the framework of projective dynamics, th…
Reduced-Order Inference with Structure-Preserving Parametrization for Bending and Rotating Systems
Yevgeniya Filanova, Igor Pontes Duff, Pawan Goyal +1
Mechanical systems are often characterized only by their response to certain loads known from experiments or simulations. The obtained data can be used for various purposes: system…
Application of operator inference to reduced-order modeling of constrained mechanical systems
Peter Benner, Yevgeniya Filanova, Igor Pontes Duff +1
Constrained mechanical systems occur in many applications, such as modeling of robots and other multibody systems. In this case, the motion is governed by a system of differential-…
Balanced Truncation of Descriptor Systems with a Quadratic Output
Jennifer Przybilla, Igor Pontes Duff, Pawan Goyal +1
This work discusses model reduction for differential-algebraic systems with quadratic output equations. Under mild conditions, these systems can be transformed into a Weierstraß ca…
Dominant Subspaces of High-Fidelity Nonlinear Structured Parametric Dynamical Systems and Model Reduction
Pawan Goyal, Igor Pontes Duff, Peter Benner
In this work, we investigate a model order reduction scheme for high-fidelity nonlinear structured parametric dynamical systems. More specifically, we consider a class of nonlinear…
An Operator Inference Oriented Approach for Mechanical Systems
Yevgeniya Filanova, Igor Pontes Duff, Pawan Goyal +1
Model-order reduction techniques allow the construction of low-dimensional surrogate models that can accelerate engineering design processes. Often, these techniques are intrusive,…
Model reduction for second-order systems with inhomogeneous initial conditions
Jennifer Przybilla, Igor Pontes Duff, Peter Benner
In this paper, we consider the problem of finding surrogate models for large-scale second-order linear time-invariant systems with inhomogeneous initial conditions. For this class…
Operator Inference and Physics-Informed Learning of Low-Dimensional Models for Incompressible Flows
Peter Benner, Pawan Goyal, Jan Heiland +1
Reduced-order modeling has a long tradition in computational fluid dynamics. The ever-increasing significance of data for the synthesis of low-order models is well reflected in the…