Learning noise

Schmidt, Michael D.; Lipson, Hod

doi:10.1145/1276958.1277289

Cited by 17 publications

(9 citation statements)

References 10 publications

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“…After this point, five of the seven states can be modeled exactly up to 30% noise, and four of the seven at 50% noise. It is worth noting that many real biological systems may also contain different types of noise, such as asymmetrical noise, and a modified equation search method could be used in these cases, such as explicitly including symbolic noise sources in the ODE model [72]. …”

Section: Methodsmentioning

confidence: 99%

Automated refinement and inference of analytical models for metabolic networks

et al. 2011

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The reverse engineering of metabolic networks from experimental data is traditionally a labor-intensive task requiring a priori systems knowledge. Using a proven model as a test system, we demonstrate an automated method to simplify this process by modifying an existing or related model – suggesting nonlinear terms and structural modifications – or even constructing a new model that agrees with the system’s time-series observations. In certain cases, this method can identify the full dynamical model from scratch without prior knowledge or structural assumptions. The algorithm selects between multiple candidate models by designing experiments to make their predictions disagree. We performed computational experiments to analyze a nonlinear seven-dimensional model of yeast glycolytic oscillations. This approach corrected mistakes reliably in both approximated and overspecified models. The method performed well to high levels of noise for most states, could identify the correct model de novo, and make better predictions than ordinary parametric regression and neural network models. We identified an invariant quantity in the model, which accurately derived kinetics and the numerical sensitivity coefficients of the system. Finally, we compared the system to dynamic flux estimation and discussed the scaling and application of this methodology to automated experiment design and control in biological systems in real-time.

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Section: Methodsmentioning

confidence: 99%

Automated refinement and inference of analytical models for metabolic networks

et al. 2011

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“…Modern approaches to symbolic regression make use of sophisticated techniques to speed up convergence and improve the quality of results: competitive co-evolution [20], incorporation of expert knowledge [21], noise modeling [22], and, most notably, Pareto-like optimization [23]. Taking inspiration from works on multi-objective optimization, Pareto-like symbolic regression evaluates candidate solutions on more than one feature, for example fitting and complexity: instead of returning the user a single, optimal solution, such algorithms will show a Pareto front comprising several solutions, each one an optimal trade-off between the two objectives.…”

Section: Symbolic Regressionmentioning

confidence: 99%

Data driven modeling of plastic deformation

Versino

Tonda

Bronkhorst

2017

Computer Methods in Applied Mechanics and Engineering

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In this paper the application of machine learning techniques for the development of constitutive material models is being investigated. A flow stress model, for strain rates ranging from 10 −4 to 10 12 (quasi-static to highly dynamic), and temperatures ranging from room temperature to over 1000 K, is obtained by beginning directly with experimental stress-strain data for Copper. An incrementally objective and fully implicit time integration scheme is employed to integrate the hypo-elastic constitutive model, which is then implemented into a finite element code for evaluation. Accuracy and performance of the flow stress models derived from symbolic regression are assessed by comparison to Taylor anvil impact data. The results obtained with the free-form constitutive material model are compared to well-established strength models such as the Preston-Tonks-Wallace (PTW) model and the Mechanical Threshold Stress (MTS) model. Preliminary results show candidate free-form models comparing well with data in regions of stress-strain space with sufficient experimental data, pointing to a potential means for both rapid prototyping in future model development, as well as the use of machine learning in capturing more data as a guide for more advanced model development.

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“…In spite of the fact that these models utilise the data as it arrives, there can still arise situations where the underlying assumptions of the model no longer hold. We call such settings dynamic environments, where changes in data distribution [1], change in features' relevance [2], non-symmetrical noise levels [3] are common. It has been shown that many changes in the environment which are no longer being reflected in the model contribute to the deterioration of model's accuracy over time [4]- [7].…”

Section: Introductionmentioning

confidence: 99%

Augmenting adaptation with retrospective model correction for non-stationary regression problems

Bakirov

Gabrys

Fay

2016

2016 International Joint Conference on Neural Networks (IJCNN)

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Abstract-Existing adaptive predictive methods often use multiple adaptive mechanisms as part of their coping strategy in nonstationary environments. We address a scenario when selective deployment of these adaptive mechanisms is possible. In this case, deploying each adaptive mechanism results in different candidate models, and only one of these candidates is chosen to make predictions on the subsequent data. After observing the error of each of candidate, it is possible to revert the current model to the one which had the least error. We call this strategy retrospective model correction. In this work we aim to investigate the benefits of such approach. As a vehicle for the investigation we use an adaptive ensemble method for regression in batch learning mode which employs several adaptive mechanisms to react to changes in the data. Using real world data from the process industry we show empirically that the retrospective model correction is indeed beneficial for the predictive accuracy, especially for the weaker adaptive mechanisms.

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Learning noise

Cited by 17 publications

References 10 publications

Automated refinement and inference of analytical models for metabolic networks

Automated refinement and inference of analytical models for metabolic networks

Data driven modeling of plastic deformation

Augmenting adaptation with retrospective model correction for non-stationary regression problems

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