A Theory of Neural Networks for Control
Develop a comprehensive mathematical theory of neural networks for control to enhance safety, robustness, and reliability in critical applications like healthcare and aerospace.
Projectdetails
Introduction
As neural networks are delivering groundbreaking performance in various machine learning frameworks—ranging from the basic framework of supervised learning to the powerful and challenging framework of control—immense efforts focus on developing underlying mathematical theories. Recent years witnessed breakthrough contributions to the theory of neural networks for supervised learning, by myself and others.
Theoretical Gaps
Yet, from a theoretical perspective, much is left to be elucidated about neural networks in the powerful framework of control. This leads to a predominantly heuristic implementation, which hinders their use in control application domains where safety, robustness, and reliability are critical, such as:
- Healthcare
- Aerospace
- Manufacturing
Research Goals
The overarching goal of the proposed research is to develop a comprehensive mathematical theory of neural networks for control. This theory will provide:
- An explanative formalism for intriguing empirical phenomena.
- Breakthrough practical techniques that promote safety, robustness, and reliability.
The research aims to overcome major current challenges by harnessing powerful mathematical tools in the realms of tensor analysis and dynamical systems theory, which I have developed over the past decade.
Unique Positioning
Building on my academic record in the theory of neural networks for supervised learning, which is accompanied by vast practical industry experience with neural networks for control, I am confident in being uniquely positioned to pursue this pressing ambitious goal of developing a practical theory for neural networks in control.
Expected Impact
A successful outcome of the research will significantly broaden the theoretical knowledge on neural networks and unleash their power in critical control application domains, thereby having a transformative impact.
Financiële details & Tijdlijn
Financiële details
Subsidiebedrag | € 1.493.750 |
Totale projectbegroting | € 1.493.750 |
Tijdlijn
Startdatum | 1-10-2024 |
Einddatum | 30-9-2029 |
Subsidiejaar | 2024 |
Partners & Locaties
Projectpartners
- TEL AVIV UNIVERSITYpenvoerder
Land(en)
Vergelijkbare projecten binnen European Research Council
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MANUNKIND: Determinants and Dynamics of Collaborative ExploitationThis project aims to develop a game theoretic framework to analyze the psychological and strategic dynamics of collaborative exploitation, informing policies to combat modern slavery. | ERC STG | € 1.497.749 | 2022 | Details |
Elucidating the phenotypic convergence of proliferation reduction under growth-induced pressureThe UnderPressure project aims to investigate how mechanical constraints from 3D crowding affect cell proliferation and signaling in various organisms, with potential applications in reducing cancer chemoresistance. | ERC STG | € 1.498.280 | 2022 | Details |
Uncovering the mechanisms of action of an antiviral bacteriumThis project aims to uncover the mechanisms behind Wolbachia's antiviral protection in insects and develop tools for studying symbiont gene function. | ERC STG | € 1.500.000 | 2023 | Details |
The Ethics of Loneliness and SociabilityThis project aims to develop a normative theory of loneliness by analyzing ethical responsibilities of individuals and societies to prevent and alleviate loneliness, establishing a new philosophical sub-field. | ERC STG | € 1.025.860 | 2023 | Details |
MANUNKIND: Determinants and Dynamics of Collaborative Exploitation
This project aims to develop a game theoretic framework to analyze the psychological and strategic dynamics of collaborative exploitation, informing policies to combat modern slavery.
Elucidating the phenotypic convergence of proliferation reduction under growth-induced pressure
The UnderPressure project aims to investigate how mechanical constraints from 3D crowding affect cell proliferation and signaling in various organisms, with potential applications in reducing cancer chemoresistance.
Uncovering the mechanisms of action of an antiviral bacterium
This project aims to uncover the mechanisms behind Wolbachia's antiviral protection in insects and develop tools for studying symbiont gene function.
The Ethics of Loneliness and Sociability
This project aims to develop a normative theory of loneliness by analyzing ethical responsibilities of individuals and societies to prevent and alleviate loneliness, establishing a new philosophical sub-field.
Vergelijkbare projecten uit andere regelingen
Project | Regeling | Bedrag | Jaar | Actie |
---|---|---|---|---|
Projection-based Control: A Novel Paradigm for High-performance SystemsPROACTHIS aims to develop a novel projection-based control paradigm to enhance performance in future engineering systems through innovative design and optimization techniques. | ERC ADG | € 2.498.516 | 2022 | Details |
Scalable Control Approximations for Resource Constrained EnvironmentsThis project aims to advance optimal control and decision-making for nonlinear processes on dynamic networks by developing new theories, algorithms, and software for various applications. | ERC COG | € 1.998.500 | 2023 | Details |
Control for Deep and Federated LearningCoDeFeL aims to enhance machine learning methods through control theory, developing efficient ResNet architectures and federated learning techniques for applications in digital medicine and recommendations. | ERC ADG | € 2.499.224 | 2024 | Details |
Projection-based Control: A Novel Paradigm for High-performance Systems
PROACTHIS aims to develop a novel projection-based control paradigm to enhance performance in future engineering systems through innovative design and optimization techniques.
Scalable Control Approximations for Resource Constrained Environments
This project aims to advance optimal control and decision-making for nonlinear processes on dynamic networks by developing new theories, algorithms, and software for various applications.
Control for Deep and Federated Learning
CoDeFeL aims to enhance machine learning methods through control theory, developing efficient ResNet architectures and federated learning techniques for applications in digital medicine and recommendations.