23 papers · ranked by Valyu relevance
Danica M. Ommen, Christopher P. Saunders
In statistics, there are a variety of methods for performing model selection that all stem from slightly different paradigms of statistical inference. The reasons for choosing one particular method over another seem to be based entirely on philosophical preferences. In the case of non-nested model selection, two of the…
Authors not listed
The increasing importance and predictive power of modern molecular modeling, driven by physics- and machine learning-based methods, necessitates a new collaborative architecture to replace the isolated, traditional model of software development. The traditional approach often led to redundant engineering effort, high…
O. Ebenhöh, M. van Aalst, N.P. Saadat, T. Nies + 1 more
The modelbase package is a free expandable Python package for building and analysing dynamic mathematical models of biological systems. Originally it was designed for the simulation of metabolic systems, but it can be used for virtually any deterministic chemical processes. modelbase provides easy construction methods…
Mo Li, Wesley W. Ingwersen, Ben Young, Jorge Vendries + 1 more
'Catherine Birney'] useeior is an open-source R package that builds USEEIO models, a family of environmentally-extended input-output models of US goods and services used for life cycle assessment, environmental footprint estimation, and related applications. USEEIO models have gained a wide user base since their…
Alexander L.R. Lubbock, Carlos F. Lopez
Computational modeling has become an established technique to encode mathematical representations of cellular processes and gain mechanistic insights that drive testable predictions. These models are often constructed using graphical user interfaces or domain-specific languages, with SBML used for interchange. Models…
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…
Kate E. Dray, Joseph J. Muldoon, Niall M. Mangan, Neda Bagheri + 1 more
Mathematical modeling is invaluable for advancing understanding and design of synthetic biological systems. However, the model development process is complicated and often unintuitive, requiring iteration on various computational tasks and comparisons with experimental data. Ad hoc model development can pose a barrier…
Chenxi Wang, Jihui Zhao, Jingjing Zheng, Barak Raveh + 2 more
Developing and optimizing models for complex systems poses challenges due to the inherent complexity introduced by multiple types of input information and sources of uncertainty. In this study, we utilize Bayesian formalism to analytically examine the propagation of probability in the modeling process and propose…
Alex Gu, Tamara Mitrovska, Daniela Velez, Jacob Andreas + 1 more
'Armando Solar-Lezama'] We introduce ObSynth, an interactive system leveraging the domain knowledge embedded in large language models (LLMs) to help users design object models from high level natural language prompts. This is an example of specification reification, the process of taking a high-level, potentially vague…
Mojeeb Al-Rhman Al-Khiaty, Moataz Ahmed
— Software reuse allows the software industry to simultaneously reduce development cost and improve product quality. Reuse of early-stage artifacts has been acknowledged to be more beneficial than reuse of later-stage artifacts. In this regard, early-stage reference models have been considered as good tools to allow…
Therese M. Donovan, Jonathan E. Katz, Esmaeil Ebrahimie
Agencies are increasingly called upon to implement their natural resource management programs within an adaptive management (AM) framework. This article provides the background and motivation for the R package, AMModels. AMModels was developed under R version 3.2.2. The overall goal of AMModels is simple: To codify…
Authors not listed
Scientific modeling often requires navigating a trade-off between physical interpretability and empirical accuracy—a task that can take weeks of iteration, especially in systems with partial observability, structural complexity, and experimental errors. Here, we show how a state-of-the-art agentic reasoning-and-coding…
Thomas Wöhling, Alvaro Oliver Crespo Delgadillo, Moritz Kraft, Anneli Guthke
'Anneli Guthke'] Title: Abstract Groundwater level observations are used as decision variables for aquifer management, often in conjunction with models to provide predictions for operational forecasting. In this study, we compare different model classes for this task: a spatially explicit 3D groundwater flow model…
Konstantinos M. Andreadis, Narendra Das, Dimitrios Stampoulis, Amor Ines + 6 more
'Amor Ines' 'Joshua B. Fisher' 'Stephanie Granger' 'Jessie Kawata' 'Eunjin Han' 'Ali Behrangi' 'Guy J-P. Schumann'] The Regional Hydrologic Extremes Assessment System (RHEAS) is a prototype software framework for hydrologic modeling and data assimilation that automates the deployment of water resources nowcasting and…
Klaus‐Dieter Sommer, P M Harris, Sascha Eichstädt, Roland Füßl + 9 more
'Tanja Dorst' 'Andreas Schütze' 'Michael Heizmann' 'Nadine Schiering' 'Andreas Maier' 'Yuhui Luo' 'Christos Tachtatzis' 'Ivan Andonović' 'Gordon Gourlay'] - 1 Technische Universitaet Ilmenau, Germany - 2 National Physical Laboratory, Teddington, United Kingdom - 3 Physikalisch-Technische Bundesanstalt, Braunschweig and…
Feng Zhu, Yiping Yao, Huilong Chen, Feng Yao
Model reuse is a key issue to be resolved in parallel and distributed simulation at present. However, component models built by different domain experts usually have diversiform interfaces, couple tightly, and bind with simulation platforms closely. As a result, they are difficult to be reused across different…
C Anthony Hunt, Glen EP Ropella, Tai ning Lam, Andrew D Gewitz
We review grounding issues that influence the scientific usefulness of any biomedical multiscale model (MSM). Groundings are the collection of units, dimensions, and/or objects to which a variable or model constituent refers. To date, models that primarily use continuous mathematics rely heavily on absolute grounding…
Benoît Combemale, Julien De Antoni, Robert B. France, Frédéric Boulanger + 5 more
'Frédéric Boulanger' 'Sébastien Mosser' 'Marc Pantel' 'Bernhard Rumpe⋆' 'Rick Salay' 'Martin Schindler'] Abstract. The first edition of GEMOC workshop was co-located with the MOD-ELS 2013 conference in Miami, FL, USA. The workshop provided an open forum for sharing experiences, problems and solutions related to the…
Christopher Schölzel, Valeria Blesius, Gernot Ernst, Andreas Dominik
Reproducible, understandable models that can be reused and combined to true multi-scale systems are required to solve the present and future challenges of systems biology. However, many mathematical models are still built for a single purpose and reusing them in a different context can be challenging due to an…
Chao Xu, Emily Mazeau, Richard West
Mean-field micro-kinetic modeling is a powerful tool for catalyst design and the simulation of catalytic processes. The reaction enthalpies in a micro-kinetic model often need to be adjusted when changing species' binding energies to model different catalysts, when performing thermodynamic sensitivity analyses, and…
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Kinetic modeling is essential for predicting changes in food quality during processing and storage. This study evaluates the application of physics-informed neural networks (PINN) for food kinetic modeling, integrating kinetic insights into neural network frameworks. Based on three case studies, namely seed drying…
Arne Haber, Markus Look, Antonio Pérez, Bernhard Rumpe⋆ + 2 more
A ModelingLanguage is a black-box language and contains language-specific information such as the file ending. It may contain either a single language or a composition of embedded languages, such as CDs with embedded HQL. Therefore, modeling languages contain a hierarchy of ILanguage interfaces. Based on this…
Aleksandr Fedorov, Anna Perechodjuk, David Linke
Artificial neural networks (ANNs) are powerful tools for solving a wide range of tasks in fundamental and applied science. However, training and building reliable ANN models requires a lot of data which so far hinders their wider application in kinetic modelling where typically only small (experimental) datasets are…