20 citations · 67 across the 24 of their papers we have counts for
26 papers
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…
Data-driven balanced truncation for linear systems with quadratic outputs
Reetish Padhi, Ion Victor Gosea, Igor Pontes Duff +1
We develop the framework for a non-intrusive, quadrature-based method for approximate balanced truncation (QuadBT) of linear systems with quadratic outputs, thus extending the appl…
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-…
Inference of Substructured Reduced-Order Models for Dynamic Contact from Contact-free Simulations
Diana Manvelyan-Stroot, Yevgeniya Filanova, Igor Pontes Duff +2
In this paper, we propose an operator-inference-based reduction approach for contact problems, leveraging snapshots from simulations without active contact. Contact problems are so…
-optimal model reduction of linear quadratic-output systems by multivariate rational interpolation
Sean Reiter, Ion Victor Gosea, Igor Pontes Duff +1
This paper addresses the -optimal approximation of linear dynamical systems with quadratic-output functions, also known as linear quadratic-output systems. Our major…
On the representation of energy-preserving quadratic operators with application to Operator Inference
Leonidas Gkimisis, Igor Pontes Duff, Pawan Goyal +1
In this work, we investigate a skew-symmetric parameterization for energy-preserving quadratic operators. Earlier, [Goyal et al., 2023] proposed this parameterization to enforce en…
Active Sampling of Interpolation Points to Identify Dominant Subspaces for Model Reduction
Celine Reddig, Pawan Goyal, Igor Pontes Duff +1
Model reduction is an active research field to construct low-dimensional surrogate models of high fidelity to accelerate engineering design cycles. In this work, we investigate mod…
optimal model reduction of linear systems with multiple quadratic outputs
Sean Reiter, Igor Pontes Duff, Ion Victor Gosea +1
In this work, we consider the optimal model reduction of dynamical systems that are linear in the state equation and up to quadratic nonlinearity in the output equation. As o…
Stability-Certified Learning of Control Systems with Quadratic Nonlinearities
Igor Pontes Duff, Pawan Goyal, Peter Benner
This work primarily focuses on an operator inference methodology aimed at constructing low-dimensional dynamical models based on a priori hypotheses about their structure, often in…
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…
Guaranteed Stable Quadratic Models and their applications in SINDy and Operator Inference
Pawan Goyal, Igor Pontes Duff, Peter Benner
Scientific machine learning for inferring dynamical systems combines data-driven modeling, physics-based modeling, and empirical knowledge. It plays an essential role in engineerin…
Inference of Continuous Linear Systems from Data with Guaranteed Stability
Pawan Goyal, Igor Pontes Duff, Peter Benner
Machine-learning technologies for learning dynamical systems from data play an important role in engineering design. This research focuses on learning continuous linear models from…
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…
Model order reduction for bilinear systems with non-zero initial states -- different approaches with error bounds
Martin Redmann, Igor Pontes Duff
In this paper, we consider model order reduction for bilinear systems with non-zero initial conditions. We discuss choices of Gramians for both the homogeneous and the inhomogeneou…
Full state approximation by Galerkin projection reduced order models for stochastic and bilinear systems
Martin Redmann, Igor Pontes Duff
In this paper, the problem of full state approximation by model reduction is studied for stochastic and bilinear systems. Our proposed approach relies on identifying the dominant s…
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…
Toward fitting structured nonlinear systems by means of dynamic mode decomposition
Ion Victor Gosea, Igor Pontes Duff
The dynamic mode decomposition (DMD) is a data-driven method used for identifying the dynamics of complex nonlinear systems. It extracts important characteristics of the underlying…
Identification of Dominant Subspaces for Linear Structured Parametric Systems and Model Reduction
Peter Benner, Pawan Goyal, Igor Pontes Duff
In this paper, we discuss a novel model reduction framework for generalized linear systems. The transfer functions of these systems are assumed to have a special structure, e.g., c…
Data-Driven Identification of Rayleigh-Damped Second-Order Systems
Igor Pontes Duff, Pawan Goyal, Peter Benner
In this paper, we present a data-driven approach to identify second-order systems, having internal Rayleigh damping. This means that the damping matrix is given as a linear combina…
Gramians, Energy Functionals and Balanced Truncation for Linear Dynamical Systems with Quadratic Outputs
Peter Benner, Pawan Goyal, Igor Pontes Duff
Model order reduction is a technique that is used to construct low-order approximations of large-scale dynamical systems. In this paper, we investigate a balancing based model orde…
New Gramians for Linear Switched Systems: Reachability, Observability, and Model Reduction
Igor Pontes Duff, Sara Grundel, Peter Benner
In this paper, we propose new algebraic Gramians for continuous-time linear switched systems, which satisfy generalized Lyapunov equations. The main contribution of this work is tw…