scholarly journals A hybrid modeling framework using dimensional analysis for erosion predictions

2021 ◽  
pp. 107577
Author(s):  
Wei Dai ◽  
Samira Mohammadi ◽  
Selen Cremaschi
Keyword(s):  
Dimensional Analysis ◽  
Hybrid Modeling ◽  
Modeling Framework

Modeling the response of non-perennial streams to climate change impact: The Bouregreg watershed in Morocco

2021 ◽  
Author(s):  
Anna Maria De Girolamo ◽  
Youssef Brouziyne ◽  
Lahcen Benaabidate ◽  
Aziz Aboubdillah ◽  
Ali El Bilali ◽  
...  
Keyword(s):  
Climate Change ◽  
Assessment Tool ◽  
Swat Model ◽  
Circulation Model ◽  
Hybrid Modeling ◽  
Climate Data ◽  
Global Circulation Model ◽  
Stream Network ◽  
Modeling Framework ◽  
Modeling Approach

<p>The non-perennial streams and rivers are predominant in the Mediterranean region and play an important ecological role in the ecosystem diversity in this region. This class of streams is particularly vulnerable to climate change effects that are expected to amplify further under most climatic projections. Understanding the potential response of the hydrologic regime attributes to climatic stress helps in planning better conservation and management strategies. Bouregreg watershed (BW) in Morocco, is a strategic watershed for the region with a developed non-perennial stream network, and with typical assets and challenges of most Mediterranean watersheds. In this study, a hybrid modeling approach, based on the Soil and Water Assessment Tool (SWAT) model and Indicator of Hydrologic Alteration (IHA) program, was used to simulate the response of BW's stream network to climate change during the period: 2035-2050. Downscaled daily climate data from the global circulation model CNRM-CM5 were used to force the hybrid modeling framework over the study area. Results showed that, under the changing climate, the magnitude of the alteration will be different across the stream network; however, almost the entire flow regime attributes will be affected. Under the RCP8.5 scenario, the average number of zero-flow days will rise up from 3 to 17.5 days per year in some streams, the timing of the maximum flow was calculated to occur earlier by 17 days than in baseline, and the timing of the minimal flow should occur later by 170 days in some streams. The used modeling approach in this study contributed in identifying the most vulnerable streams in the BW to climate change for potential prioritization in conservation plans.</p>

scholarly journals A novel yeast hybrid modeling framework integrating Boolean and enzyme-constrained networks enables exploration of the interplay between signaling and metabolism

2020 ◽  
Author(s):  
Linnea Österberg ◽  
Iván Domenzain ◽  
Julia Münch ◽  
Jens Nielsen ◽  
Stefan Hohmann ◽  
...  
Keyword(s):  
Neurological Disorders ◽  
Regulatory Network ◽  
Glucose Repression ◽  
Hybrid Modeling ◽  
Human Diseases ◽  
Crabtree Effect ◽  
Modeling Framework ◽  
Chronological Lifespan ◽  
Regulatory Effects ◽  
Constraint Model

AbstractThe interplay between nutrient-induced signaling and metabolism plays an important role in maintaining homeostasis and its malfunction has been implicated in many different human diseases such as obesity, type 2 diabetes, cancer and neurological disorders. Therefore, unravelling the role of nutrients as signaling molecules and metabolites as well as their interconnectivity may provide a deeper understanding of how these conditions occur. Both signalling and metabolism have been extensively studied using various systems biology approaches. However, they are mainly studied individually and in addition current models lack both the complexity of the dynamics and the effects of the crosstalk in the signaling system. To gain a better understanding of the interconnectivity between nutrient signaling and metabolism, we developed a hybrid model, combining Boolean model, describing the signalling layer and the enzyme constraint model accounting for metabolism using a regulatory network as a link. The model was capable of reproducing the regulatory effects that are associated with the Crabtree effect and glucose repression. We show that using this methodology one can investigat intrinsically different systems, such as signaling and metabolism, in the same model and gain insight into how the interplay between them can have non-trivial effects by showing a connection between Snf1 signaling and chronological lifespan by the regulation of NDE and NDI usage in respiring conditions. In addition, the model showed that during fermentation, enzyme utilization is the more important factor governing the protein allocation, while in low glucose conditions robustness and control is prioritized.Author summaryElucidating the complex relationship between nutrient-induced signaling and metabolism represents a key in understanding the onset of many different human diseases like obesity, type 3 diabetes, cancer and many neurological disorders. In this work we proposed a hybrid modeling approach, combining Boolean representation of singaling pathways, like Snf11, TORC1 and PKA with the enzyme constrained model of metabolism linking them via the regulatory network. This allowed us to improve individual model predictions and elucidate how single components in the dynamic signaling layer affect the steady-state metabolism. The model has been tested under respiration and fermentation, reveling novel connections and further reproducing the regulatory effects that are associated with the Crabtree effect and glucose repression. Finally, we show a connection between Snf1 signaling and chronological lifespan by the regulation of NDE and NDI usage in respiring conditions.

scholarly journals Functional-Hybrid Modeling through automated adaptive symbolic regression for interpretable mathematical expressions

2021 ◽  
Author(s):  
Harini Narayanan ◽  
M. Nicolas Cruz Bournazou ◽  
Gonzalo Guillen-Gosalbez ◽  
Alessandro Butte
Keyword(s):  
Hybrid Model ◽  
Dynamic Models ◽  
Learning Algorithm ◽  
Symbolic Regression ◽  
Hybrid Modeling ◽  
Data Driven ◽  
Support Vector ◽  
Successful Implementation ◽  
Modeling Framework ◽  
Improved Performance

Mathematical models used for the representation of (bio)-chemical processes can be grouped into two broad paradigms: white-box or mechanistic models, completely based on knowledge or black-box data-driven models based on patterns observed in data. However, in the past two-decade, hybrid modeling that explores the synergy between the two paradigms has emerged as a pragmatic compromise. The data-driven part of these have been largely based on conventional machine learning algorithm (e.g., artificial neural network, support vector regression), which prevents interpretability of the finally learnt model by the domain-experts. In this work we present a novel hybrid modeling framework, the Functional-Hybrid model, that uses the ranked domain-specific functional beliefs together with symbolic regression to develop dynamic models. We demonstrate the successful implementation of these hybrid models for four benchmark systems and a microbial fermentation reactor, all of which are systems of (bio)chemical relevance. We also demonstrate that compared to a similar implementation with the conventional ANN, the performance of Functional-Hybrid model is at least two times better in interpolation and extrapolation. Additionally, the proposed framework can learn the dynamics in 50% lower number of experiments. This improved performance can be attributed to the structure imposed by the functional transformations introduced in the Functional-Hybrid model.

Characterizing the transduction behavior of ionic polymer-metal composite actuators and sensors via dimensional analysis

2021 ◽  
Author(s):  
Zakai Olsen ◽  
Kwang Jin Kim
Keyword(s):  
Dimensional Analysis ◽  
Nonlinear Regression ◽  
Smart Materials ◽  
Regression Models ◽  
Short Circuit ◽  
Metal Composite ◽  
Modeling Framework ◽  
Ionic Polymer ◽  
Nonlinear Regression Models ◽  
Polymer Metal

Abstract Ionic polymer-metal composites (IPMCs) are functional smart materials that exhibit both electromechanical and mechanoelectrical transduction properties, and the physical phenomenon underlying the transduction mechanisms have been studied across the literature extensively. Here we use a new modeling framework to conduct the most comprehensive dimensional analysis of IPMC transduction phenomena, characterizing the IPMC actuator displacement, actuator blocking force, short-circuit sensing current, and open-circuit sensing voltage under static and dynamic loading. The information obtained in this analysis is used to construct nonlinear regression models for the transduction response as univariant and multivariant functions. Automatic differentiation techniques are leveraged to linearize the nonlinear regression models in the vicinity of a typical IPMC description and derive the sensitivity of the transduction response with respect to the driving independent variables. Further, the multiphysics model is validated using experimental data collected for the dynamic IPMC actuator and voltage sensor. With data collected from physical samples of IPMC materials in-lab, the regression models developed under the new computational framework are verified.

scholarly journals A Hybrid Modeling Framework for Membrane Separation Processes: Application to Lithium-Ion Recovery from Batteries

Processes ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1939
Author(s):  
Maria João Regufe ◽  
Vinicius V. Santana ◽  
Alexandre F. P. Ferreira ◽  
Ana M. Ribeiro ◽  
José M. Loureiro ◽  
...  
Keyword(s):  
Hybrid Model ◽  
Phenomenological Model ◽  
Membrane Separation ◽  
Lithium Ion ◽  
Hybrid Modeling ◽  
Modeling Framework ◽  
Separation Processes ◽  
Transfer Resistance ◽  
Model Training ◽  
Transient System

This study proposed a hybrid modeling framework for membrane separation processes where lithium from batteries is recovered. This is a pertinent problem nowadays as lithium batteries are popularized in hybrid and electric vehicles. The hybrid model is based on an artificial intelligence (AI) structure to model the mass transfer resistance of several experimental separations found in the literature. It is also based on a phenomenological model to represent the transient system regime. An optimization framework was designed to perform the AI model training and simultaneously solve the Ordinary Differential Equation (ODE) system representing the phenomenological model. The results demonstrate that the hybrid model can better represent the experimental validation sets than the phenomenological model alone. This strategy opens doors for further investigations of this system.

Sign in / Sign up

Export Citation Format

Share Document