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18 papers · ranked by Valyu relevance

01
AarXiv21 Jul 2017
Upscaling Uncertainty with Dynamic Discrepancy for a Multi-Scale Carbon Capture System
K. Sham Bhat, David S. Mebane, Priyadarshi Mahapatra, Curtis B. Storlie
'Curtis B. Storlie'] Uncertainties from model parameters and model discrepancy from small-scale models impact the accuracy and reliability of predictions of large-scale systems. Inadequate representation of these uncertainties may result in inaccurate and overconfident predictions during scale-up to larger models.…
83relevance
02
PPubMed16 Dec 2025
A Framework for Implementation of Dynamic Discrepancy Reduced-Order Modeling in Advanced Process Control
San Dinh, Claudemi A. Nascimento, David S. Mebane, Fernando V. Lima
Implementation of Dynamic Discrepancy Reduced-Order Modeling in Advanced Process Control Authors: San Dinh, Claudemi A. Nascimento, David S. Mebane, Fernando V. Lima This paper introduces a novel framework for implementing dynamic discrepancy reduced-order modeling in advanced process control. This framework balances…
82relevance
03
AarXiv2022
Online Discrepancy with Recourse for Vectors and Graphs
Anupam Gupta, Vijaykrishna Gurunathan, Ravishankar Krishnaswamy, Amit Kumar + 1 more
'Amit Kumar' 'Sahil Singla'] e vector-balancing problem is a fundamental problem in discrepancy theory: given vectors in [−1, 1] , nd a signing () ∈ {±1} of each vector to minimize the discrepancy k Í () · k∞. is problem has been extensively studied in the static/oine seing. In this paper we initiate its study in the…
76relevance
04
AarXiv25 Jan 2024
Discrepancy Modeling Framework: Learning Missing Physics, Modeling Systematic Residuals, and Disambiguating between Deterministic and Random Effects
Megan R. Ebers, Katherine M. Steele, J. Nathan Kutz
Physics-based and first-principles models pervade the engineering and physical sciences, allowing for the ability to model the dynamics of complex systems with a prescribed accuracy. The approximations used in deriving governing equations often result in discrepancies between the model and sensor-based measurements of…
74relevance
05
BbioRxiv11 Apr 2022
A maximum mean discrepancy approach reveals subtle changes in *α*-synuclein dynamics
Hippolyte Verdier, François Laurent, Alhassan Cassé, Christian L. Vestergaard + 2 more
Numerous models have been developed to account for the complex properties of the random walks of biomolecules. However, when analysing experimental data, conditions are rarely met to ensure model identification. The dynamics may simultaneously be influenced by spatial and temporal heterogeneities of the environment…
70relevance
06
BbioRxiv21 Jan 2023
A machine learning approach to quantify individual gait responses to ankle exoskeletons
Megan R. Ebers, Michael C. Rosenberg, J. Nathan Kutz, Katherine M. Steele
We currently lack a theoretical framework capable of characterizing heterogeneous responses to exoskeleton interventions. Predicting an individual’s response to an exoskeleton and understanding what data are needed to characterize responses has been a persistent challenge. In this study, we leverage a neural…
70relevance
07
PPubMed25 May 2020
Considering discrepancy when calibrating a mechanistic electrophysiology model Discrepancy and mechanistic modelling
Chon Lok Lei, Sanmitra Ghosh, Dominic G. Whittaker, Yasser Aboelkassem + 13 more
Uncertainty quantification (UQ) is a vital step in using mathematical models and simulations to take decisions. The field of cardiac simulation has begun to explore and adopt UQ methods to characterize uncertainty in model inputs and how that propagates through to outputs or predictions; examples of this can be seen in…
68relevance
08
PPubMed18 Nov 2020
Data-Driven Corrections of Partial Lotka–Volterra Models
Rebecca E. Morrison
In many applications of interacting systems, we are only interested in the dynamic behavior of a subset of all possible active species. For example, this is true in combustion models (many transient chemical species are not of interest in a given reaction) and in epidemiological models (only certain subpopulations are…
66relevance
09
PPubMed11 Feb 2016
Learning (from) the errors of a systems biology model
Benjamin Engelhardt, Holger Frőhlich, Maik Kschischo
Mathematical modelling is a labour intensive process involving several iterations of testing on real data and manual model modifications. In biology, the domain knowledge guiding model development is in many cases itself incomplete and uncertain. A major problem in this context is that biological systems are open.…
64relevance
10
PPubMed6 Feb 2025
Systematic structural discrepancy assessment for computer models Systematic structural discrepancy assessment for computer models
Michael Goldstein, Ian Vernon, Jonathan A. Cumming
Model or structural discrepancy is an essential component in the analysis of computer simulators, representing the differences between the outputs of the simulator and the real-world system that the simulator seeks to represent. This discrepancy can arise from various sources such as simplifications of the model…
64relevance
11
AarXiv2019
Embedded discrepancy operators in reduced models of interacting species
Rebecca E. Morrison
- Abstract. In many applications of interacting systems, we are only interested in the dynamic behavior of a subset of all possible active species. For example, this is true in combustion models (many transient chemical species are not of interest in a given reaction) and in epidemiological models (only certain…
63relevance
12
PPubMed24 Nov 2023
Empirical Quantification of Predictive Uncertainty Due to Model Discrepancy by Training with an Ensemble of Experimental Designs: An Application to Ion Channel Kinetics
Joseph G. Shuttleworth, Chon Lok Lei, Dominic G. Whittaker, Monique J. Windley + 3 more
'Monique J. Windley' 'Adam P. Hill' 'Simon P. Preston' 'Gary R. Mirams'] When using mathematical models to make quantitative predictions for clinical or industrial use, it is important that predictions come with a reliable estimate of their accuracy (uncertainty quantification). Because models of complex biological…
62relevance
13
CchemRxiv23 May 2023
3DDPDs: Describing protein dynamics for proteochemometric bioactivity prediction. A case for (mutant) G protein-coupled receptors
Marina Gorostiola González, Remco L. van den Broek, Thomas G.M. Braun, Magdalini Chatzopoulou + 4 more
Proteochemometric (PCM) modelling is a powerful computational drug discovery tool used in bioactivity prediction of potential drug candidates relying on both chemical and protein information. In PCM features are computed to describe small molecules and proteins, which directly impact the quality of the predictive…
57relevance
14
BbioRxiv22 Feb 2019
Precision-weighting of superior frontal cortex unsigned prediction error signals benefits learning, is mediated by dopamine, and is impaired in psychosis
J. Haarsma, P.C. Fletcher, J.D. Griffin, H.J. Taverne + 5 more
Recent theories of cortical function construe the brain as performing hierarchical Bayesian inference. According to these theories, the precision of cortical unsigned prediction error (i.e., surprise) signals plays a key role in learning and decision-making, to be controlled by dopamine, and to contribute to the…
55relevance
15
BbioRxiv1 Jan 2019
Analyzing dynamic decision-making models using Chapman-Kolmogorov equations
Nicholas W. Barendregt, Krešimir Josić, Zachary P. Kilpatrick
Decision-making in dynamic environments typically requires adaptive evidence accumulation that weights new evidence more heavily than old observations. Recent experimental studies of dynamic decision tasks require subjects to make decisions for which the correct choice switches stochastically throughout a single trial.…
54relevance
16
CchemRxiv10 Jul 2025
Automated Generation of Mechanistic Models for Chemical Process Digital Twins using Reinforcement Learning - Part I: Conceptual Framework and Equation Generation
Authors not listed
Deriving versatile and robust mechanistic models from experimental data is a key challenge in engineering and natural sciences. This is especially true in chemical reaction engineering, where reactor manufacturers and operators increasingly pursue the development and maintenance of digital twins that rely on frequent…
52relevance
17
CchemRxiv23 Aug 2024
Smoother Semiclassical Dispersion for Density Functional Theory via D3S: Understanding and Addressing Unphysical Minima in the D3 Dispersion Correction Model
Nikolay Tkachenko, Martin Head-Gordon
One of the most widely used and computationally efficient models that accounts for London dispersion interactions within density functional theory (DFT) is the D3 dispersion correction model. In this work, we demonstrate that this model can induce the appearance of unphysical minima on the potential energy surface…
51relevance
18
BbioRxiv2 Nov 2016
Dynamic Modeling, Parameter Estimation and Uncertainty Analysis in 𝗥
Daniel Kaschek, Wolfgang Mader, Mirjam Fehling-Kaschek, Marcus Rosenblatt + 1 more
In a wide variety of research elds, dynamic modeling is employed as an instrument to learn and understand complex systems. The differential equations involved in this process are usually non-linear and depend on many parameters whose values decide upon the characteristics of the emergent system. The inverse problem…
50relevance

Results for dynamic discrepancy

Upscaling Uncertainty with Dynamic Discrepancy for a Multi-Scale Carbon Capture System

A Framework for Implementation of Dynamic Discrepancy Reduced-Order Modeling in Advanced Process Control

Online Discrepancy with Recourse for Vectors and Graphs

Discrepancy Modeling Framework: Learning Missing Physics, Modeling Systematic Residuals, and Disambiguating between Deterministic and Random Effects

A maximum mean discrepancy approach reveals subtle changes in α-synuclein dynamics

A machine learning approach to quantify individual gait responses to ankle exoskeletons

Considering discrepancy when calibrating a mechanistic electrophysiology model Discrepancy and mechanistic modelling

Data-Driven Corrections of Partial Lotka–Volterra Models

Learning (from) the errors of a systems biology model

Systematic structural discrepancy assessment for computer models Systematic structural discrepancy assessment for computer models

Embedded discrepancy operators in reduced models of interacting species

Empirical Quantification of Predictive Uncertainty Due to Model Discrepancy by Training with an Ensemble of Experimental Designs: An Application to Ion Channel Kinetics

3DDPDs: Describing protein dynamics for proteochemometric bioactivity prediction. A case for (mutant) G protein-coupled receptors

Precision-weighting of superior frontal cortex unsigned prediction error signals benefits learning, is mediated by dopamine, and is impaired in psychosis

Analyzing dynamic decision-making models using Chapman-Kolmogorov equations

Automated Generation of Mechanistic Models for Chemical Process Digital Twins using Reinforcement Learning - Part I: Conceptual Framework and Equation Generation

Smoother Semiclassical Dispersion for Density Functional Theory via D3S: Understanding and Addressing Unphysical Minima in the D3 Dispersion Correction Model

Dynamic Modeling, Parameter Estimation and Uncertainty Analysis in 𝗥

Upscaling Uncertainty with Dynamic Discrepancy for a Multi-Scale Carbon Capture System

A Framework for Implementation of Dynamic Discrepancy Reduced-Order Modeling in Advanced Process Control

Online Discrepancy with Recourse for Vectors and Graphs

Discrepancy Modeling Framework: Learning Missing Physics, Modeling Systematic Residuals, and Disambiguating between Deterministic and Random Effects

A maximum mean discrepancy approach reveals subtle changes in α-synuclein dynamics

A machine learning approach to quantify individual gait responses to ankle exoskeletons

Considering discrepancy when calibrating a mechanistic electrophysiology model Discrepancy and mechanistic modelling

Data-Driven Corrections of Partial Lotka–Volterra Models

Learning (from) the errors of a systems biology model

Systematic structural discrepancy assessment for computer models Systematic structural discrepancy assessment for computer models

Embedded discrepancy operators in reduced models of interacting species

Empirical Quantification of Predictive Uncertainty Due to Model Discrepancy by Training with an Ensemble of Experimental Designs: An Application to Ion Channel Kinetics

3DDPDs: Describing protein dynamics for proteochemometric bioactivity prediction. A case for (mutant) G protein-coupled receptors

Precision-weighting of superior frontal cortex unsigned prediction error signals benefits learning, is mediated by dopamine, and is impaired in psychosis

Analyzing dynamic decision-making models using Chapman-Kolmogorov equations

Automated Generation of Mechanistic Models for Chemical Process Digital Twins using Reinforcement Learning - Part I: Conceptual Framework and Equation Generation

Smoother Semiclassical Dispersion for Density Functional Theory via D3S: Understanding and Addressing Unphysical Minima in the D3 Dispersion Correction Model

Dynamic Modeling, Parameter Estimation and Uncertainty Analysis in 𝗥

Results for dynamic discrepancy

Upscaling Uncertainty with Dynamic Discrepancy for a Multi-Scale Carbon Capture System

A Framework for Implementation of Dynamic Discrepancy Reduced-Order Modeling in Advanced Process Control

Online Discrepancy with Recourse for Vectors and Graphs

Discrepancy Modeling Framework: Learning Missing Physics, Modeling Systematic Residuals, and Disambiguating between Deterministic and Random Effects

A maximum mean discrepancy approach reveals subtle changes in *α*-synuclein dynamics

A machine learning approach to quantify individual gait responses to ankle exoskeletons

Considering discrepancy when calibrating a mechanistic electrophysiology model Discrepancy and mechanistic modelling

Data-Driven Corrections of Partial Lotka–Volterra Models

Learning (from) the errors of a systems biology model

Systematic structural discrepancy assessment for computer models Systematic structural discrepancy assessment for computer models

Embedded discrepancy operators in reduced models of interacting species

Empirical Quantification of Predictive Uncertainty Due to Model Discrepancy by Training with an Ensemble of Experimental Designs: An Application to Ion Channel Kinetics

3DDPDs: Describing protein dynamics for proteochemometric bioactivity prediction. A case for (mutant) G protein-coupled receptors

Precision-weighting of superior frontal cortex unsigned prediction error signals benefits learning, is mediated by dopamine, and is impaired in psychosis

Analyzing dynamic decision-making models using Chapman-Kolmogorov equations

Automated Generation of Mechanistic Models for Chemical Process Digital Twins using Reinforcement Learning - Part I: Conceptual Framework and Equation Generation

Smoother Semiclassical Dispersion for Density Functional Theory via D3S: Understanding and Addressing Unphysical Minima in the D3 Dispersion Correction Model

Dynamic Modeling, Parameter Estimation and Uncertainty Analysis in 𝗥

Upscaling Uncertainty with Dynamic Discrepancy for a Multi-Scale Carbon Capture System

A Framework for Implementation of Dynamic Discrepancy Reduced-Order Modeling in Advanced Process Control

Online Discrepancy with Recourse for Vectors and Graphs

Discrepancy Modeling Framework: Learning Missing Physics, Modeling Systematic Residuals, and Disambiguating between Deterministic and Random Effects

A maximum mean discrepancy approach reveals subtle changes in *α*-synuclein dynamics

A machine learning approach to quantify individual gait responses to ankle exoskeletons

Considering discrepancy when calibrating a mechanistic electrophysiology model Discrepancy and mechanistic modelling

Data-Driven Corrections of Partial Lotka–Volterra Models

Learning (from) the errors of a systems biology model

Systematic structural discrepancy assessment for computer models Systematic structural discrepancy assessment for computer models

Embedded discrepancy operators in reduced models of interacting species

Empirical Quantification of Predictive Uncertainty Due to Model Discrepancy by Training with an Ensemble of Experimental Designs: An Application to Ion Channel Kinetics

3DDPDs: Describing protein dynamics for proteochemometric bioactivity prediction. A case for (mutant) G protein-coupled receptors

Precision-weighting of superior frontal cortex unsigned prediction error signals benefits learning, is mediated by dopamine, and is impaired in psychosis

Analyzing dynamic decision-making models using Chapman-Kolmogorov equations

Automated Generation of Mechanistic Models for Chemical Process Digital Twins using Reinforcement Learning - Part I: Conceptual Framework and Equation Generation

Smoother Semiclassical Dispersion for Density Functional Theory via D3S: Understanding and Addressing Unphysical Minima in the D3 Dispersion Correction Model

Dynamic Modeling, Parameter Estimation and Uncertainty Analysis in 𝗥

A maximum mean discrepancy approach reveals subtle changes in α-synuclein dynamics

A maximum mean discrepancy approach reveals subtle changes in α-synuclein dynamics