Zobrazeno 1 - 10
of 8 107
pro vyhledávání: '"Benner, P."'
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 model reduction for linear structured systems using dominant reachable an
Externí odkaz:
http://arxiv.org/abs/2409.03892
Autor:
Khorrami, Mohammad S., Goyal, Pawan, Mianroodi, Jaber R., Svendsen, Bob, Benner, Peter, Raabe, Dierk
The purpose of the current work is the development and comparison of Fourier neural operators (FNOs) for surrogate modeling of the quasi-static mechanical response of polycrystalline materials. Three types of such FNOs are considered here: a physics-
Externí odkaz:
http://arxiv.org/abs/2408.15408
Transient gas network simulations can significantly assist in design and operational aspects of gas networks. Models used in these simulations require a detailed framework integrating various models of the network constituents - pipes and compressor
Externí odkaz:
http://arxiv.org/abs/2406.01164
Autor:
Forootani, Ali, Benner, Peter
The sparse identification of nonlinear dynamical systems (SINDy) is a data-driven technique employed for uncovering and representing the fundamental dynamics of intricate systems based on observational data. However, a primary obstacle in the discove
Externí odkaz:
http://arxiv.org/abs/2405.08613
Partial differential equation parameter estimation is a mathematical and computational process used to estimate the unknown parameters in a partial differential equation model from observational data. This paper employs a greedy sampling approach bas
Externí odkaz:
http://arxiv.org/abs/2405.08537
Publikováno v:
IEEE Control Systems Letters, 2024
This paper aims at characterizing the approximability of bounded sets in the range of nonlinear operators in Banach spaces by finite-dimensional linear varieties. In particular, the class of operators we consider includes the endpoint maps of nonline
Externí odkaz:
http://arxiv.org/abs/2403.06029
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 informed by established physics or expert insights. Stability is a funda
Externí odkaz:
http://arxiv.org/abs/2403.00646
Numerical algorithms and computational tools are instrumental in navigating and addressing complex simulation and data processing tasks. The exponential growth of metadata and parameter-driven simulations has led to an increasing demand for automated
Externí odkaz:
http://arxiv.org/abs/2405.00028
Autor:
Gosea, Ion Victor, Peterson, Luisa, Goyal, Pawan, Bremer, Jens, Sundmacher, Kai, Benner, Peter
In this work, we address the challenge of efficiently modeling dynamical systems in process engineering. We use reduced-order model learning, specifically operator inference. This is a non-intrusive, data-driven method for learning dynamical systems
Externí odkaz:
http://arxiv.org/abs/2402.17698
This work discusses model reduction for differential-algebraic systems with quadratic output equations. Under mild conditions, these systems can be transformed into a Weierstra{\ss} canonical form and, thus, be decoupled into differential equations a
Externí odkaz:
http://arxiv.org/abs/2402.14716