Search · four archives
Search · four archives
22 papers · ranked by Valyu relevance
Deepanjhan Das, Vishwesh Ramanathan, Shankar Narasimhan
The dynamic behavior of numerous engineering processes is effectively characterized through differential-algebraic equations (DAEs), commonly referred to as descriptor systems. While substantial progress has been achieved in identifying dynamic models governed by ordinary differential equations (ODEs), limited research…
Padoan, A., Eising, J. + 2 more
The paper extends core results of behavioral systems theory from linear to affine time-invariant systems. We characterize the behavior of affine time-invariant systems via kernel, input–output, state-space, and finite-horizon data-driven representations, demonstrating a range of structural parallels with linear…
Amir Hossein Salehi Shayegan
In this work, we present a solution to the critical limitation of qubit capacity in near-term quantum hardware by giving a hybrid framework that integrates the spectral element method (SEM) with distributed quantum computing. Using domain decomposition techniques, the additive and multiplicative Schwarz methods, the…
Hyun Dong Lee, Aditi Jha, Stephen E. Clarke, Michael P. Silvernagel + 2 more
Understanding neural dynamics is crucial for uncovering how the brain processes information and controls behavior. Linear dynamical systems (LDS) are widely used for modeling neural data due to their simplicity and effectiveness in capturing latent dynamics. However, LDS assumes a stable mapping from the latent states…
Carla White, Vivi Rottschäfer, Lloyd Bridge
Quantification of drug-cell interactions and subsequent cellular responses by using experimental data together with mathematical models of assumed binding and signalling schematics is vital to many research programmes; data fitting provides estimates for important pharmacological parameters including kinetic parameters…
Farooq, Farhana, Danish Rafiq
The increasing size and complexity of modern power systems have led to a high-dimensional mathematical model for transient stability studies, rendering full-scale simulations computationally burdensome. While dimensionality reduction is essential for reducing this complexity, conventional approaches in power systems…
Ignacio Tapia García, Cristóbal Torrealba, Ricardo Luna, José Ricardo Pérez-Correa + 1 more
Dynamic Flux Balance Analysis (DFBA) enables simulation of microbial culture dynamics under changing environmental conditions, but remains computationally expensive for tasks such as parameter calibration and fermentation optimization when applied using genome-scale metabolic models (GEMs). To address this challenge…
Stefan Müller, Georg Regensburger
We provide fundamental results on positive solutions to parametrized systems of generalized polynomial inequalities (with real exponents and positive parameters), including generalized polynomial equations. In doing so, we also offer a new perspective on fewnomials and (generalized) mass-action systems. We find that…
Amir Hossein Salehi Shayegan
Time-fractional diffusion equations have emerged as powerful models for describing anomalous transport phenomena in physics, biology and engineering. To address the computational challenges arising from their non-local operators, we employ the WEB-spline finite element method, which provides a flexible and accurate…
Martin Redmann
We investigate quadratic bilinear systems by developing novel tree-based representations of their solutions. The proposed framework decomposes the solution into a sequence of coupled bilinear subsystems whose components admit explicit expansions indexed by full binary trees. These representations yield sufficient…
Douglas R. Frey
This paper presents a unifying theory of Linear second order systems that allows time-varying and time invariant systems to be treated in the same way for the first time. In the process, a transformation is given that diagonalizes an arbitrary time varying state matrix in a spectrum invariant way. A canonical form for…
Yiteng Zhang, Zixiong Wang, Xingyu Li, Bin Min
Inferring computational mechanisms from neural recordings is a central goal in systems neuro-science. Recent developments have identified low-rank recurrent neural networks (RNNs) as an effective tool for fitting observed neural activity and extracting neural dynamics. However, we show that accurate activity fitting…
Olga Oskina, Alexey Bobtsov
This paper addresses the problem of observer design for a class of linear descriptor systems affected by a certain class of unknown unmatched disturbances. The objective is to estimate the components of the state vector, as well as the unknown parameters of the unmeasured disturbance. To solve this problem, structural…
Luis Albrizzi, Gabriel Gayoso, José Colbes, Santiago Di Lella + 2 more
Torsional Dynamics: An Angular-Displacement PCA Pipeline for Short-Horizon Prediction from Molecular Dynamics Authors: Luis Albrizzi, Gabriel Gayoso, José Colbes, Santiago Di Lella, Christian E. Schaerer, Amaury C. Alvarez Analyzing the conformational dynamics of short peptides from molecular dynamics (MD) simulations…
Papalia, Alan, Sanderson, Nikolas + 8 more
— Robotic perception often requires solving large nonlinear least-squares (NLS) problems. While sparsity has been well-exploited to scale solvers, a complementary and underexploited structure is separability – where some variables (e.g., visual landmarks) appear linearly in the residuals and, for any estimate of the…
Kazuo Ishii, Bishnu Prasad Gautam, Jieling Wu, Javaid Saher
We characterize observability in representation learning through Platonic Projection Structures (PPS), an operator-theoretic framework for analyzing representation accessibility under partial observation. Rather than treating observable outputs as direct reflections of latent representations, PPS models observation as…
A. Marraffa, R. Krause, V. Mante, G. Haller
Artificial Recurrent Neural Networks (RNNs) are widely used in neuroscience to model the collective activity of neurons during behavioral tasks. The high dimensionality of their parameter and activity spaces, however, often make it challenging to infer and interpret the fundamental features of their dynamics. In this…
Authors not listed
In this work, we present an investigation of the role of basis set size on linear response (LR) calculations of electronic properties of extended systems using density functional theory with periodic boundary conditions (DFT-PBC) and Gaussian-type atomic orbital (GTO) bases. We report results of electric…
Authors not listed
In this work, we implement a local-pair natural orbital based coupled-cluster method through the full treatment of quadruple excitations (CCSDTQ). The domain-based local pair natural orbital (DLPNO) approach, which has successfully been applied to lower levels of coupled-cluster theory, is utilized in our algorithm…
Teddy Lazebnik, Alex Liberzon
Symbolic Regression (SR) is a powerful technique for discovering analytical mathematical expressions that describe observed numerical data. Traditionally, SR models work on data in tabular form, imposing a purely functional mapping without considering the underlying spatio-temporal dependencies or the governing…
Andrea Polo-Rodríguez, David R. Penas, Julio R. Banga
Parameter estimation is a central challenge in systems biology, particularly for large dynamic models described by nonlinear ordinary differential equations (ODEs). These global optimization problems exhibit landscapes which are topologically heterogeneous, often exhibiting a pathological mixture of stiff, smooth…
Authors not listed
Most advances in electronic spin-dependent non-adiabatic dynamics focus on refining the underlying dynamics methods. In contrast, this work considers an improved description of spin-orbit coupling by explicitly accounting for its relativistic origins. To this end, we extend a standard one-electron triatomic…