Simulation-based parameter identification of a reduced model using neural networks

2011 ◽  
Author(s):  
M.G.A. Nassef ◽  
C. Schenck ◽  
B. Kuhfuss
Keyword(s):  
Neural Networks ◽  
Parameter Identification ◽  
Reduced Model ◽  
Simulation Based

scholarly journals DeltaDelta Neural Networks for Lead Optimization of Small Molecule Potency

2019 ◽  
Author(s):  
Jose Jimenez-Luna ◽  
Laura Pérez-Benito ◽  
Gerard Martinez-Rosell ◽  
Simone Sciabola ◽  
Rubben Torella ◽  
...  
Keyword(s):  
Neural Networks ◽  
Lead Optimization ◽  
Learning Approach ◽  
Potential Drug ◽  
Protein Target ◽  
Drug Molecules ◽  
Simulation Based ◽  
Machine Learning Approach ◽  
Fundamental Challenge ◽  
Deep Learning Model

The capability to rank different potential drug molecules against a protein target for potency has always been a fundamental challenge in computational chemistry due to its importance in drug design. While several simulation-based methodologies exist, they are hard to use prospectively and thus predicting potency in lead optimization campaigns remains an open challenge. Here we present the first machine learning approach specifically tailored for ranking ranking congeneric series based on deep 3D-convolutional neural networks. Furthermore we prove its effectiveness by blindly testing it on datasets provided by Janssen, Pfizer and Biogen totalling over 3246 ligands and 13 targets as well as several well-known openly available sets, representing one the largest evaluations ever performed. We also performed online learning simulations of lead optimization using the approach in a predictive manner obtaining significant advantage over experimental choice. We believe that the evaluation performed in this study is strong evidence of the usefulness of a modern deep learning model in lead optimization pipelines against more expensive simulation-based alternatives.

Fourier Series-Based Neural Networks for Vehicle System Identification

1999 ◽  
Author(s):  
Imtiaz Haque ◽  
Juergen Schuller
Keyword(s):  
Neural Networks ◽  
Fourier Series ◽  
System Identification ◽  
Transfer Function ◽  
Parameter Identification ◽  
Frequency Domain ◽  
Vehicle System ◽  
Non Linear ◽  
Function Identification ◽  
Non Linear System

Abstract The use of neural networks in system identification is an emerging field. Neural networks have become popular in recent years as a means to identify linear and non-linear systems whose characteristics are unknown. The success of sigmoidal networks in parameter identification has been limited. However, harmonic activation-based neural networks, recent arrivals in the field of neural networks, have shown excellent promise in linear and non-linear system parameter identification. They have been shown to have excellent generalization capability, computational parallelism, absence of local minima, and good convergence properties. They can be used in the time and frequency domain. This paper presents the application of a special class of such networks, namely Fourier Series neural networks (FSNN) to vehicle system identification. In this paper, the applications of the FSNNs are limited to the frequency domain. Two examples are presented. The results of the identification are based on simulation data. The first example demonstrates the transfer function identification of a two-degree-of freedom lateral dynamics model of an automobile. The second example involves transfer function identification for a quarter car model. The network set-up for such identification is described. The results of the network identification are compared with theory. The results indicate excellent prediction properties of such networks.

Simulation-based deep artifact correction with Convolutional Neural Networks for limited angle artifacts

2019 ◽  
Vol 29 (2) ◽  
pp. 150-161 ◽  
Author(s):  
Alena-Kathrin Schnurr ◽  
Khanlian Chung ◽  
Tom Russ ◽  
Lothar R. Schad ◽  
Frank G. Zöllner
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Artifact Correction ◽  
Simulation Based ◽  
Limited Angle

Deep Learning-based Surrogate Models for Water Distribution Systems

2020 ◽  
Author(s):  
Riccardo Taormina ◽  
Mohammad Ashrafi ◽  
Andres Murillo ◽  
Stefano Galelli
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems ◽  
Surrogate Models ◽  
Water Network ◽  
Control Logic ◽  
Simulation Based ◽  
Simulation Based Optimization

<p><span>Simulation-based optimization is widely used for designing and managing water distribution networks. The process involves the use of accurate computational models, such as EPANET, which represent the physical processes taking place in the water network and reproduce the control logic governing its operations. Unfortunately, running such models requires expensive computations, which, in turn, may hinder the application of simulation-based optimization to large and complex problems. This issue can be overcome by resorting to surrogate models, that is, simplified data-driven models that accurately mimic the behaviours of physical-based models at a fraction of the computational costs. In this work, we explore the potential of Deep Learning Neural Networks (DLNN) for building surrogate models for water distribution systems. Different DLNN architectures, including feed-forward and recurrent neural networks, are trained and validated on datasets generated through EPANET simulations. The DLNN models are then used in lieu of the original EPANET model to speed-up the evaluation of the objective function employed in a simulation-based optimization problem. The effectiveness of the proposed technique is assessed on a realistic case-study involving cyber-attacks on a water network. In particular, the DLNN surrogate models are employed by an evolutionary optimization algorithm that schedules the operations of hydraulic actuators in order to best respond to the attacks and facilitate the recovery process.</span></p>

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