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Showing 1 to 12 of 12 for “"Low-dimensional Models"”.

  1. Low-dimensional Models for Real-time Simulation of Internal Combustion Engines and Catalytic After-treatment Systems

    … Such an optimization can be accomplished using low-order fundamentals (first-principles) based models for each of the engine sub-systems, i.e. in-cylinder combustion processes, exhaust after-treatment systems, mechanical and electrical systems (for hybrid vehicles) and sensor and control …

    houston Repository record for Low-dimensional Models for Real-time Simulation of Internal Combustion Engines and Catalytic After-treatment Systems (opens in a new tab)

  2. Balance control and locomotion planning for humanoid robots using nonlinear centroidal models

    … for humanoid robots have traditionally relied on low-dimensional models for locomotion planning and reactive balance control. Results for the low-dimensional model are mapped to the full robot, and used as inputs to a whole-body controller. In particular, the linear inverted pendulum (LIP) has …

    mit Repository record for Balance control and locomotion planning for humanoid robots using nonlinear centroidal models (opens in a new tab)

  3. Physics-based machine learning and data-driven reduced-order modeling

    … thesis considers the task of learning efficient low-dimensional models for dynamical systems. To be effective in an engineering setting, these models must be predictive -- that is, they must yield reliable predictions for conditions outside the data used to train them. These models must also be …

    mit Repository record for Physics-based machine learning and data-driven reduced-order modeling (opens in a new tab)

  4. Development of Data-driven Models to Predict Vs30 from mHVSR

    … in the upper 30 meters (VS30) using data-driven models. We develop a dataset comprising 536 sites with 2,861 three-component ambient noise recordings from global regions, including New Zealand, Taiwan, Italy, Ecuador, Mexico and the United States. The identically processed three-component ambient …

    vt Repository record for Development of Data-driven Models to Predict Vs30 from mHVSR (opens in a new tab)

  5. Investigating the Interaction of a Supersonic Single Expansion Ramp Nozzle and Sonic Wall Jet

    … supersonic jets, have exacerbated the need for flow physics research. Supersonic flight remains the standard for military aircraft and is being rediscovered for commercial use. With the addition of multiple streams, complex nozzle geometries, and airframe integration in modern aircraft, the flow …

    syracuse-diss Repository record for Investigating the Interaction of a Supersonic Single Expansion Ramp Nozzle and Sonic Wall Jet (opens in a new tab)

  6. A dynamical-systems approach to understanding turbulence in plane Couette flow

    … theory is used to understand the dynamics of low-dimensional spatio-temporal chaos. Our research aimed to apply the theory to understanding turbulent fluid flows, which could be thought of as spatio-temporal chaos in a very-high dimensional space. The theory explains a system's dynamics in …

    unh-thes Repository record for A dynamical-systems approach to understanding turbulence in plane Couette flow (opens in a new tab)

  7. Modeling and Estimation of Motion Over Manifolds with Motion Capture Data

    … first study in this dissertation aims to build low-dimensional models models from motion capture data. This study also expands on the so-called learning problem from statistical learning theory over Euclidean spaces to estimating functions over manifolds. Experimental results are presented for …

    vt Repository record for Modeling and Estimation of Motion Over Manifolds with Motion Capture Data (opens in a new tab)

  8. Robust spectral representations and model inference for biological dynamics

    … developments in automated experimental imaging allow for high-resolution tracking across various scales, from whole animal behavior to single-cell dynamics to spatiotemporal gene expression. Transforming these high-dimensional data into effective low-dimensional models is an essential theoretical …

    mit Repository record for Robust spectral representations and model inference for biological dynamics (opens in a new tab)

  9. Nonparametric Methods for Analysis and Modeling of Complex Multivariate Distributions

    … Traditional, ``parametric'' statistical models and methods are limited, either in their ability to capture nuances that cannot be generated by low dimensional models or in applying restrictive assumptions to inferential procedures that are rarely met. In this work, we present three novel …

    duke Repository record for Nonparametric Methods for Analysis and Modeling of Complex Multivariate Distributions (opens in a new tab)

  10. Adaptive error estimation in linearized ocean general circulation models

    … MT) method of adaptive error estimation with low-dimensional models. We then apply the MT method in the North Pacific (5°-60° N, 132°-252° E) to TOPEX/POSEIDON sea level anomaly data, acoustic tomography data from the ATOC project, and the MIT General Circulation Model (GCM). A reduced state …

    woods-hole Repository record for Adaptive error estimation in linearized ocean general circulation models (opens in a new tab)

  11. Adaptive error estimation in linearized ocean general circulation models

    … MT) method of adaptive error estimation with low-dimensional models. We then apply the MT method in the North Pacific (5°-60° N, 132°-252° E) to TOPEX/POSEIDON sea level anomaly data, acoustic tomography data from the ATOC project, and the MIT General Circulation Model (GCM). A reduced state …

    mit Repository record for Adaptive error estimation in linearized ocean general circulation models (opens in a new tab)

  12. High-dimensional MR spectroscopic imaging integrating physics-based modeling and machine learning

    Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-12-01

    uiuc Repository record for High-dimensional MR spectroscopic imaging integrating physics-based modeling and machine learning (opens in a new tab)