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A Physics-Informed Manifold Neural Operator Framework for Multi-Parameter Prediction of Polymer Aging in HTPB Solid Propellants. [PDF]
Liu S +5 more
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Research on precision prediction of heavy-duty lathes based on hybrid PINNS neural network. [PDF]
Wang R, Jing H, Yang M.
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Nonlinear Contributions of NO <sub><i>x</i></sub> and Volatile Chemical Products to Air Pollution and the Associated Acute Premature Mortality. [PDF]
Liu J, Capps SL.
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TF-DWGNet: a directed weighted graph neural network with tensor fusion for multi-omics cancer subtype classification. [PDF]
Yang T, Chen Z.
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Reduced order modeling in iTOUGH2
Computers & Geosciences, 2014The inverse modeling and uncertainty quantification capabilities of iTOUGH2 are augmented with reduced order models (ROMs) that act as efficient surrogates for computationally expensive high fidelity models (HFMs). The implementation of the ROM capabilities involves integration of three main computational components.
George Shu Heng Pau +4 more
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On Synthesis of Reduced Order Models
2011A framework for model reduction and synthesis is presented, which enables the re-use of reduced order models in circuit simulation. Two synthesis techniques are considered for obtaining the circuit representation (netlist) of the reduced model: (1) by means of realizing the reduced transfer function and (2) by unstamping the reduced system matrices ...
Ionutiu, R., Rommes, J.
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Reduced-order modeling for hyperthermia control
IEEE Transactions on Biomedical Engineering, 1992This paper analyzes the feasibility of using reduced-order modeling techniques in the design of multiple-input, multiple-output (MIMO) hyperthermia temperature controllers. State space thermal models are created based upon a finite difference expansion of the bioheat transfer equation model of a scanned focused ultrasound system (SFUS).
J. Kyle Potocki, Hal S. Tharp
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Reduced-Order Models for MEMS Applications
Nonlinear Dynamics, 2005zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Nayfeh, Ali H. +2 more
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