Optimization of a Hydrodynamic Computational Reservoir through Evolution
- URL: http://arxiv.org/abs/2304.10610v1
- Date: Thu, 20 Apr 2023 19:15:02 GMT
- Title: Optimization of a Hydrodynamic Computational Reservoir through Evolution
- Authors: Alessandro Pierro, Kristine Heiney, Shamit Shrivastava, Giulia
Marcucci, Stefano Nichele
- Abstract summary: We interface with a model of a hydrodynamic system, under development by a startup, as a computational reservoir.
We optimized the readout times and how inputs are mapped to the wave amplitude or frequency using an evolutionary search algorithm.
Applying evolutionary methods to this reservoir system substantially improved separability on an XNOR task, in comparison to implementations with hand-selected parameters.
- Score: 58.720142291102135
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: As demand for computational resources reaches unprecedented levels, research
is expanding into the use of complex material substrates for computing. In this
study, we interface with a model of a hydrodynamic system, under development by
a startup, as a computational reservoir and optimize its properties using an
evolution in materio approach. Input data are encoded as waves applied to our
shallow water reservoir, and the readout wave height is obtained at a fixed
detection point. We optimized the readout times and how inputs are mapped to
the wave amplitude or frequency using an evolutionary search algorithm, with
the objective of maximizing the system's ability to linearly separate
observations in the training data by maximizing the readout matrix determinant.
Applying evolutionary methods to this reservoir system substantially improved
separability on an XNOR task, in comparison to implementations with
hand-selected parameters. We also applied our approach to a regression task and
show that our approach improves out-of-sample accuracy. Results from this study
will inform how we interface with the physical reservoir in future work, and we
will use these methods to continue to optimize other aspects of the physical
implementation of this system as a computational reservoir.
Related papers
- Fusion of Gaussian Processes Predictions with Monte Carlo Sampling [61.31380086717422]
In science and engineering, we often work with models designed for accurate prediction of variables of interest.
Recognizing that these models are approximations of reality, it becomes desirable to apply multiple models to the same data and integrate their outcomes.
arXiv Detail & Related papers (2024-03-03T04:21:21Z) - FAStEN: an efficient adaptive method for feature selection and
estimation in high-dimensional functional regressions [8.384075654211685]
We propose a new, flexible and ultra-efficient approach to perform feature selection in a sparse function-on-function regression problem.
We show how to extend it to the scalar-on-function framework.
We present an application to brain fMRI data from the AOMIC PIOP1 study.
arXiv Detail & Related papers (2023-03-26T19:41:17Z) - Spectral learning of Bernoulli linear dynamical systems models [21.3534487101893]
We develop a learning method for fast, efficient fitting of latent linear dynamical system models.
Our approach extends traditional subspace identification methods to the Bernoulli setting.
We show that the estimator provides real world settings by analyzing data from mice performing a sensory decision-making task.
arXiv Detail & Related papers (2023-03-03T16:29:12Z) - TunaOil: A Tuning Algorithm Strategy for Reservoir Simulation Workloads [0.9940728137241215]
TunaOil is a new methodology to enhance the search for optimal numerical parameters of reservoir flow simulations.
We leverage ensembles of models in different oracles to extract information from each simulation and optimize the numerical parameters in their subsequent runs.
Our experiments show that the predictions can improve the overall workload on average by 31%.
arXiv Detail & Related papers (2022-08-04T12:11:13Z) - Deep Equilibrium Assisted Block Sparse Coding of Inter-dependent
Signals: Application to Hyperspectral Imaging [71.57324258813675]
A dataset of inter-dependent signals is defined as a matrix whose columns demonstrate strong dependencies.
A neural network is employed to act as structure prior and reveal the underlying signal interdependencies.
Deep unrolling and Deep equilibrium based algorithms are developed, forming highly interpretable and concise deep-learning-based architectures.
arXiv Detail & Related papers (2022-03-29T21:00:39Z) - Adapting reservoir computing to solve the Schr\"odinger equation [0.0]
Reservoir computing is a machine learning algorithm that excels at predicting the evolution of time series.
We adapt this methodology to integrate the time-dependent Schr"odinger equation, propagating an initial wavefunction in time.
arXiv Detail & Related papers (2022-02-12T19:28:11Z) - Hybrid and Automated Machine Learning Approaches for Oil Fields
Development: the Case Study of Volve Field, North Sea [58.720142291102135]
The paper describes the usage of intelligent approaches for field development tasks that may assist a decision-making process.
We focus on the problem of wells location optimization and two tasks within it: improving the quality of oil production estimation and estimation of reservoir characteristics.
The implemented approaches can be used to analyze different oil fields or adapted to similar physics-related problems.
arXiv Detail & Related papers (2021-03-03T18:51:46Z) - Using Data Assimilation to Train a Hybrid Forecast System that Combines
Machine-Learning and Knowledge-Based Components [52.77024349608834]
We consider the problem of data-assisted forecasting of chaotic dynamical systems when the available data is noisy partial measurements.
We show that by using partial measurements of the state of the dynamical system, we can train a machine learning model to improve predictions made by an imperfect knowledge-based model.
arXiv Detail & Related papers (2021-02-15T19:56:48Z) - Surface Warping Incorporating Machine Learning Assisted Domain
Likelihood Estimation: A New Paradigm in Mine Geology Modelling and
Automation [68.8204255655161]
A Bayesian warping technique has been proposed to reshape modeled surfaces based on geochemical and spatial constraints imposed by newly acquired blasthole data.
This paper focuses on incorporating machine learning in this warping framework to make the likelihood generalizable.
Its foundation is laid by a Bayesian computation in which the geological domain likelihood given the chemistry, p(g|c) plays a similar role to p(y(c)|g.
arXiv Detail & Related papers (2021-02-15T10:37:52Z) - Single-step deep reinforcement learning for open-loop control of laminar
and turbulent flows [0.0]
This research gauges the ability of deep reinforcement learning (DRL) techniques to assist the optimization and control of fluid mechanical systems.
It combines a novel, "degenerate" version of the prototypical policy optimization (PPO) algorithm, that trains a neural network in optimizing the system only once per learning episode.
arXiv Detail & Related papers (2020-06-04T16:11:26Z)
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.