22 papers · ranked by Valyu relevance
Qi, Daniel C., Oguri, Kenshiro
A common approach for controller design in nonlinear systems is to linearize the dynamics about a reference trajectory and apply linear control techniques within the vicinity of the reference [[2]]. Naturally, the omitted nonlinear terms from the linearization process will result in some inconsistencies between the…
Yao Wang, Hongyu Sun, Jiakun Li, Chenlong Ma + 4 more
Highlights What are the main findings?1. We proposed a self-supervised learning paradigm that leverages frequency-domain priors to generate high-confidence pseudo-labels for identifying nonlinear errors in heterodyne interferometric sensing systems. 2. We developed a Physics-Informed Neural Network (PINN) featuring a…
Brock D. Sherlock, Marko A. A. Boon, Maria Vlasiou, Adelle C. F. Coster
Measurement error is an unavoidable feature of experimental data collection. It is common in mathematical biology to consider measurement error in the dependent variable. However, less attention has been given to errors in the independent variable. This work is focussed on the effects of independent variable…
Stefano Berrone, Moreno Pintore, Gioana Teora
We propose two globally continuous neural-based variants of the Neural Approximated Virtual Element Method (NAVEM), termed B-NAVEM and P-NAVEM. Both approaches construct local basis functions using pre-trained fully connected neural networks while ensuring exact continuity across adjacent mesh elements. B-NAVEM…
Jinhao Ke, Chenglin Wen
In networked sensing systems, nonlinear state monitoring and soft sensing are widely used to reconstruct key variables that cannot be directly measured in real time. For such nonlinear estimation tasks, the Extended Kalman Filter (EKF) is a commonly used recursive method. However, the conventional EKF neglects…
Kai Zhang, Tao Xiao, Weizong Wang, Bijiao He
Particle-in-Cell (PIC) simulations rely on accurate solutions of the electrostatic Poisson equation, yet accuracy often deteriorates near irregular Dirichlet boundaries on Cartesian meshes. While much research has addressed discretization errors on the left-hand side (LHS) of the Poisson equation, the impact of…
Isabel Garon, Stephen Keeley, Alex H. Williams
Internal neural representations can systematically deviate from externally measured sensory and behavioral variables, yet neuroscientists lack a principled statistical framework to quantify these mismatches. Here we introduce a nonlinear error-in-variables regression framework that explicitly models neural activity as…
Authors not listed
The rapid growth of worldwide computing power has transformed in silico chemistry into a discipline that is integrated into the daily work of many chemists. Nowadays, researchers find it increasingly straightforward to predict a wide range of molecular properties and chemi- cal processes at reasonable computational…
Xiaoliang Feng, Jiawei Zhang, Mohammad Reza Rahimi Tabar
This paper investigates state estimation for strongly nonlinear systems with unknown inputs under non-Gaussian heavy-tailed impulsive noise. Conventional recursive three-step filters (RTSF) based on the minimum-variance criterion are sensitive to outliers, while a single local linearization is often inadequate for…
Mudassir Shams, Andrei Velichko, Bruno Carpentieri
Efficient computation of all distinct solutions of nonlinear problems is essential in many scientific and engineering applications. Although high-order parallel iterative schemes offer fast convergence, their practical performance is often limited by sensitivity to internal parameters and the lack of reproducible…
H. van de Beek, M. Beldjenna, M. Fidler, L.B. Zwep + 1 more
Asymptotic standard errors for the parameters of a nonlinear mixed-effects model fitted by first-order conditional estimation (FOCE) or FOCE with interaction (FOCEI) require the observed (Fisher) information — the negative second derivative of the population objective at the optimum. The gradient of this objective can…
Gbenga T. Awojinrin, Abdul-Akeem Olawoyin, Rami M. Younis
We present a numerical method for the forward solution of nonlinear partial differential equations (PDEs) in which Bellman-Kalaba quasilinearization reduces the nonlinear problem to a sequence of linear subproblems, each discretized by collocation onto a trial space that is linear in its parameters and solved by a…
Mahdi Dehshiri, Kerlyns Martinez, Lauri Viitasaari
This paper develops a framework for the error analysis in nonparametric model fitting of fractional stochastic differential equations based on discrete observations. We identify and quantify the main error sources -- time discretization, coefficient approximation, and model fitting error -- within a unified framework.…
Authors not listed
Nonlinear monotonically increasing bounded functions help to visualize and analyze data on various scales. However, many monotonic functions such as logarithm or power laws have either function values or derivatives that become unbounded at some regions of the $x-$ axis. On the other hand, sigmoid or hyperbolic…
Chikoo Oosawa
Biochemical Systems Theory (BST) represents nonlinear biochemical rate laws by local power-law approximations in logarithmic concentration coordinates. First-order coefficients are elasticities, whereas higher-order derivatives describe local log-synergism and its variation. Ordinary higher derivatives, however, are…
Patrick F. Bloniasz, Emily P. Stephen
The power spectra of neural voltage recordings vary systematically across brain states and contain both narrowband (rhythmic) and broadband components. A large class of algorithms seeks to parametrize these spectra by separating rhythms from broadband structure, enabling many robust empirical findings. Here we show…
Yeeren I. Low
D. B. Brückner et al. [Phys. Rev. Lett. 125, 058103 (2020)] have described a novel method for inferring the dynamics of systems governed by an underdamped Langevin equation in the presence of measurement noise. While this is a significant achievement, the paper also presents a number of significant errors. These are…
Ping Chen, Robert J. Bauer, Yan Li
Population pharmacokinetic (popPK) models are commonly developed using ordinary differential equations (ODEs) to describe deterministic concentration–time profiles, with unexplained variability typically attributed to interindividual variability or residual error. When model misspecification is present, system-level…
Zhaohui Fan, Gandong Liu, Bo Peng, Jinyong Chen + 1 more
Shore-based maritime surveillance radars suffer from systematic range and azimuth errors that degrade target-tracking accuracy. This paper proposes a Vision Transformer (ViT) variant that corrects these errors using Automatic Identification System (AIS) data as the ground truth, modelling nonlinear error patterns via…
Authors not listed
Machine learning (ML) models are increasingly used in quantum chemistry, but their reliability hinges on uncertainty quantification (UQ). In this study, we compare two prominent UQ paradigms—Deep Evidential Regression (DER) and Deep Ensembles—on the QM9 and WS22 datasets, with a specific emphasis on the role of post…
Authors not listed
The initial formation of secondary aerosols, a large cause of uncertainty in modern radiative forcing modeling, can be simulated using quantum chemical methods. When based on quantum chemistry, the simulations have an exponential dependence on the free energy, requiring a high-accuracy description. In this study, we…
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Bayesian optimization (BO) has become increasingly important for experimental optimization across scientific domains, yet implementing BO pipelines requires significant programming expertise and familiarity with specialized frameworks. This creates a barrier for domain experts who could benefit from BO but lack the…