Deep Learning for Fast Inference of Mechanistic Models' Parameters
- URL: http://arxiv.org/abs/2312.03166v1
- Date: Tue, 5 Dec 2023 22:16:54 GMT
- Title: Deep Learning for Fast Inference of Mechanistic Models' Parameters
- Authors: Maxim Borisyak, Stefan Born, Peter Neubauer and Mariano Nicolas
Cruz-Bournazou
- Abstract summary: We propose using Deep Neural Networks (NN) for directly predicting parameters of mechanistic models given observations.
We consider a training procedure that combines Neural Networks and mechanistic models.
We find that, while Neural Network estimates are slightly improved by further fitting, these estimates are measurably better than the fitting procedure alone.
- Score: 0.28675177318965045
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Inferring parameters of macro-kinetic growth models, typically represented by
Ordinary Differential Equations (ODE), from the experimental data is a crucial
step in bioprocess engineering. Conventionally, estimates of the parameters are
obtained by fitting the mechanistic model to observations. Fitting, however,
requires a significant computational power. Specifically, during the
development of new bioprocesses that use previously unknown organisms or
strains, efficient, robust, and computationally cheap methods for parameter
estimation are of great value. In this work, we propose using Deep Neural
Networks (NN) for directly predicting parameters of mechanistic models given
observations. The approach requires spending computational resources for
training a NN, nonetheless, once trained, such a network can provide parameter
estimates orders of magnitude faster than conventional methods. We consider a
training procedure that combines Neural Networks and mechanistic models. We
demonstrate the performance of the proposed algorithms on data sampled from
several mechanistic models used in bioengineering describing a typical
industrial batch process and compare the proposed method, a typical
gradient-based fitting procedure, and the combination of the two. We find that,
while Neural Network estimates are slightly improved by further fitting, these
estimates are measurably better than the fitting procedure alone.
Related papers
- Online Variational Sequential Monte Carlo [49.97673761305336]
We build upon the variational sequential Monte Carlo (VSMC) method, which provides computationally efficient and accurate model parameter estimation and Bayesian latent-state inference.
Online VSMC is capable of performing efficiently, entirely on-the-fly, both parameter estimation and particle proposal adaptation.
arXiv Detail & Related papers (2023-12-19T21:45:38Z) - FaDIn: Fast Discretized Inference for Hawkes Processes with General
Parametric Kernels [82.53569355337586]
This work offers an efficient solution to temporal point processes inference using general parametric kernels with finite support.
The method's effectiveness is evaluated by modeling the occurrence of stimuli-induced patterns from brain signals recorded with magnetoencephalography (MEG)
Results show that the proposed approach leads to an improved estimation of pattern latency than the state-of-the-art.
arXiv Detail & Related papers (2022-10-10T12:35:02Z) - Neural parameter calibration for large-scale multi-agent models [0.7734726150561089]
We present a method to retrieve accurate probability densities for parameters using neural equations.
The two combined create a powerful tool that can quickly estimate densities on model parameters, even for very large systems.
arXiv Detail & Related papers (2022-09-27T17:36:26Z) - Learning to Learn with Generative Models of Neural Network Checkpoints [71.06722933442956]
We construct a dataset of neural network checkpoints and train a generative model on the parameters.
We find that our approach successfully generates parameters for a wide range of loss prompts.
We apply our method to different neural network architectures and tasks in supervised and reinforcement learning.
arXiv Detail & Related papers (2022-09-26T17:59:58Z) - On the Influence of Enforcing Model Identifiability on Learning dynamics
of Gaussian Mixture Models [14.759688428864159]
We propose a technique for extracting submodels from singular models.
Our method enforces model identifiability during training.
We show how the method can be applied to more complex models like deep neural networks.
arXiv Detail & Related papers (2022-06-17T07:50:22Z) - Neural Networks for Parameter Estimation in Intractable Models [0.0]
We show how to estimate parameters from max-stable processes, where inference is exceptionally challenging.
We use data from model simulations as input and train deep neural networks to learn statistical parameters.
arXiv Detail & Related papers (2021-07-29T21:59:48Z) - Physics-constrained deep neural network method for estimating parameters
in a redox flow battery [68.8204255655161]
We present a physics-constrained deep neural network (PCDNN) method for parameter estimation in the zero-dimensional (0D) model of the vanadium flow battery (VRFB)
We show that the PCDNN method can estimate model parameters for a range of operating conditions and improve the 0D model prediction of voltage.
We also demonstrate that the PCDNN approach has an improved generalization ability for estimating parameter values for operating conditions not used in the training.
arXiv Detail & Related papers (2021-06-21T23:42:58Z) - Real-time Forecast Models for TBM Load Parameters Based on Machine
Learning Methods [6.247628933072029]
In this paper, based on in-situ TBM operational data, we use the machine-learning (ML) methods to build the real-time forecast models for TBM load parameters.
To decrease the model complexity and improve the generalization, we also apply the least absolute shrinkage and selection (Lasso) method to extract the essential features of the forecast task.
arXiv Detail & Related papers (2021-04-12T07:31:39Z) - On the Sparsity of Neural Machine Translation Models [65.49762428553345]
We investigate whether redundant parameters can be reused to achieve better performance.
Experiments and analyses are systematically conducted on different datasets and NMT architectures.
arXiv Detail & Related papers (2020-10-06T11:47:20Z) - Provably Efficient Neural Estimation of Structural Equation Model: An
Adversarial Approach [144.21892195917758]
We study estimation in a class of generalized Structural equation models (SEMs)
We formulate the linear operator equation as a min-max game, where both players are parameterized by neural networks (NNs), and learn the parameters of these neural networks using a gradient descent.
For the first time we provide a tractable estimation procedure for SEMs based on NNs with provable convergence and without the need for sample splitting.
arXiv Detail & Related papers (2020-07-02T17:55:47Z) - Introduction to Rare-Event Predictive Modeling for Inferential
Statisticians -- A Hands-On Application in the Prediction of Breakthrough
Patents [0.0]
We introduce a machine learning (ML) approach to quantitative analysis geared towards optimizing the predictive performance.
We discuss the potential synergies between the two fields against the backdrop of this, at first glance, target-incompatibility.
We are providing a hands-on predictive modeling introduction for a quantitative social science audience while aiming at demystifying computer science jargon.
arXiv Detail & Related papers (2020-03-30T13:06:25Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.