Leveraging Arbitrary Mobile Sensor Trajectories With Shallow Recurrent Decoder Networks for Full-State Reconstruction

Ebers, Megan R.; Williams, Jan P.; Steele, Katherine M.; Nathan Kutz, J.

doi:10.1109/access.2024.3423679

IEEE Access

2024

DOI: 10.1109/access.2024.3423679

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Leveraging Arbitrary Mobile Sensor Trajectories With Shallow Recurrent Decoder Networks for Full-State Reconstruction

Megan R. Ebers,

Jan P. Williams,

Katherine M. Steele

et al.

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2024

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Cited by 2 publications

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Digital twins for electric propulsion technologies

Reza,

Faraji,

Knoll

2024

J Electr Propuls

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As the space industry is undergoing an evolution, the current approaches toward design, development, and qualification of Electric Propulsion (EP) systems largely based on empirical “trial-and-error” methodologies are falling short of addressing the emerging needs and keeping abreast of the rapid changes in market trends. Furthermore, with the proliferation of Artificial Intelligence (AI) within the space industry toward next-generation autonomous satellites and spacecrafts, the conventional EP monitoring and control strategies become inadequate and need to give way to approaches compatible with satellite-level autonomy requirements. A digital twin (DT) – a technology capable of providing an accurate dynamically adapting virtual representation of a physical asset – is a game-changing concept that catalyzes the transcendence of the EP industry past its pressing challenges today. In this paper, we aim to: (i) define the DT concept, highlighting how it surpasses traditional modelling, (ii) enumerate the DT’s breakthrough promises for the EP industry, and (iii) specify the challenges to realize practical and scalable EP DTs. Additionally, we report on the technical progress achieved and/or planned at Imperial Plasma Propulsion Laboratory to fill the foundational gaps in three building block elements of DTs, namely, (i) a cost-effective kinetic model to generate extensive high-fidelity databases for machine learning (ML), (ii) ML-enabled models for prediction and analysis of performance and operational behavior, and (iii) a DT architecture that integrates the numerical models in terms of a computing infrastructure and provides data pipelines and interfaces for the DT’s data exchanges with the real world, its dynamic updating, and uncertainty quantification.

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Digital twins for electric propulsion technologies

Reza,

Faraji,

Knoll

2024

J Electr Propuls

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Data-driven inference of high-dimensional spatiotemporal state of plasma systems

Reza,

Faraji,

Kutz

2024

Journal of Applied Physics

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Many plasma systems and technologies, such as Hall thrusters for spacecraft propulsion, exhibit complex underlying physics that affect the global operation. When characterizing such systems in an experiment, obtaining full spatiotemporal maps of the involved state variables can be, thus, highly informative. However, this goal is not practically realizable because of various experimental limitations, e.g., finite spatial resolution of the diagnostics and geometrical accessibility constraints. Therefore, having the capability to reconstruct the full high-dimensional states of plasma systems from low-dimensional time-history measurements is greatly desirable. Compressed sensing is a signal processing technique that can answer this crucial need. However, existing compressed sensing approaches have several limitations that restrict their effectiveness for complex physical systems like plasma technologies. These include the need for abundant sensor measurements and a principled sensor placement. In this paper, we demonstrate the capabilities of Shallow Recurrent Decoder (SHRED) architecture for compressed sensing. We show in several plasma test cases that SHRED can robustly infer full high-dimensional spatiotemporal state vectors of these systems (i.e., all macroscopic plasma properties) from minimal system information. This minimal information can consist of three finite time-history measurements of either local values of a plasma property or the global plasma properties (spatially averaged or performance parameters). An application of SHRED's inference capability in the numerical plasma simulation context is “super-resolution” enhancement. We will discuss this application by presenting how SHRED can effectively establish mappings between a low-resolution and a high-resolution simulation, recovering detailed spatial plasma features that are below the simulation's grid size.

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Leveraging Arbitrary Mobile Sensor Trajectories With Shallow Recurrent Decoder Networks for Full-State Reconstruction

Cited by 2 publications

References 40 publications

Digital twins for electric propulsion technologies

Digital twins for electric propulsion technologies

Data-driven inference of high-dimensional spatiotemporal state of plasma systems

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