Christopher Eldred
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View article: Variational Neural Networks for Observable Thermodynamics (V-NOTS)
Variational Neural Networks for Observable Thermodynamics (V-NOTS) Open
Much attention has recently been devoted to data-based computing of evolution of physical systems. In such approaches, information about data points from past trajectories in phase space is used to reconstruct the equations of motion and t…
View article: CLPNets: Coupled Lie–Poisson neural networks for multi-part Hamiltonian systems with symmetries
CLPNets: Coupled Lie–Poisson neural networks for multi-part Hamiltonian systems with symmetries Open
To accurately compute data-based prediction of Hamiltonian systems, it is essential to utilize methods that preserve the structure of the equations over time. We consider a particularly challenging case of systems with interacting parts th…
View article: Geometric, Variational, and Bracket Descriptions of Fluid Motion with Open Boundaries
Geometric, Variational, and Bracket Descriptions of Fluid Motion with Open Boundaries Open
We develop a Lie group geometric framework for the motion of fluids with permeable boundaries that extends Arnold's geometric description of fluid in closed domains. Our setting is based on the classical Hamilton principle applied to fluid…
View article: CLPNets: Coupled Lie-Poisson Neural Networks for Multi-Part Hamiltonian Systems with Symmetries
CLPNets: Coupled Lie-Poisson Neural Networks for Multi-Part Hamiltonian Systems with Symmetries Open
To accurately compute data-based prediction of Hamiltonian systems, especially the long-term evolution of such systems, it is essential to utilize methods that preserve the structure of the equations over time. We consider a case that is p…
View article: Energy-conserving physics for nonhydrostatic dynamics in mass coordinate models
Energy-conserving physics for nonhydrostatic dynamics in mass coordinate models Open
Motivated by reducing errors in the energy budget related to enthalpy fluxes within the Energy Exascale Earth System Model (E3SM), we study several physics–dynamics coupling approaches. Using idealized physics, a moist rising bubble test c…
View article: Lie–Poisson Neural Networks (LPNets): Data-based computing of Hamiltonian systems with symmetries
Lie–Poisson Neural Networks (LPNets): Data-based computing of Hamiltonian systems with symmetries Open
An accurate data-based prediction of the long-term evolution of Hamiltonian systems requires a network that preserves the appropriate structure under each time step. Every Hamiltonian system contains two essential ingredients: the Poisson …
View article: Comment on gmd-2023-184
Comment on gmd-2023-184 Open
Abstract. Motivated by reducing errors in the energy budget related to enthalpy fluxes with E3SM, we study several physics-dynamics coupling approaches. Using idealized physics, a moist rising bubble test case, and E3SM's nonhydrostatic dy…
View article: Comment on gmd-2023-184
Comment on gmd-2023-184 Open
Abstract. Motivated by reducing errors in the energy budget related to enthalpy fluxes with E3SM, we study several physics-dynamics coupling approaches. Using idealized physics, a moist rising bubble test case, and E3SM's nonhydrostatic dy…
View article: Investigating Inherent Numerical Stabilization for the Moist, Compressible, Non‐Hydrostatic Euler Equations on Collocated Grids
Investigating Inherent Numerical Stabilization for the Moist, Compressible, Non‐Hydrostatic Euler Equations on Collocated Grids Open
This study investigates inherent numerical dissipation due to upwind fluxes and reconstruction strategies for collocated Finite‐Volume integration of the Euler equations. Idealized supercell simulations are used without any explicit dissip…
View article: Energy conserving physics for nonhydrostatic dynamics in mass coordinate models
Energy conserving physics for nonhydrostatic dynamics in mass coordinate models Open
Motivated by reducing errors in the energy budget related to enthalpy fluxes with E3SM, we study several physics-dynamics coupling approaches. Using idealized physics, a moist rising bubble test case, and E3SM's nonhydrostatic dynamical co…
View article: Lie-Poisson Neural Networks (LPNets): Data-Based Computing of Hamiltonian Systems with Symmetries
Lie-Poisson Neural Networks (LPNets): Data-Based Computing of Hamiltonian Systems with Symmetries Open
An accurate data-based prediction of the long-term evolution of Hamiltonian systems requires a network that preserves the appropriate structure under each time step. Every Hamiltonian system contains two essential ingredients: the Poisson …
View article: Horizontal Resolution Sensitivity of the Simple Convection‐Permitting E3SM Atmosphere Model in a Doubly‐Periodic Configuration
Horizontal Resolution Sensitivity of the Simple Convection‐Permitting E3SM Atmosphere Model in a Doubly‐Periodic Configuration Open
We develop a doubly periodic version of the Simple Convection‐Permitting E3SM Atmosphere Model (SCREAM) to provide an efficient configuration for this global convection permitting model (GCPM), akin to a single column model often found in …
View article: The Horizontal Resolution Sensitivity of the Simple Convection-Permitting E3SM Atmosphere Model in a Doubly-Periodic Configuration
The Horizontal Resolution Sensitivity of the Simple Convection-Permitting E3SM Atmosphere Model in a Doubly-Periodic Configuration Open
We develop a doubly periodic version of the Simple Convection-Permitting E3SM Atmosphere Model (SCREAM) to provide an “efficient” configuration for this global storm resolving model (GSRM), akin to a single column model (SCM) often found i…
View article: An interpretation of TRiSK-type schemes from a discrete exterior calculus perspective
An interpretation of TRiSK-type schemes from a discrete exterior calculus perspective Open
TRiSK-type numerical schemes are widely used in both atmospheric and oceanic dynamical cores, due to their discrete analogues of important properties such as energy conservation and steady geostrophic modes. In this work, we show that thes…
View article: Differential geometric approaches to momentum-based formulations for fluids [Slides]
Differential geometric approaches to momentum-based formulations for fluids [Slides] Open
This SAND report documents CIS Late Start LDRD Project 22-0311, "Differential geometric approaches to momentum-based formulations for fluids". The project primarily developed geometric mechanics formulations for momentum-based descriptions…
View article: Reconciling and Improving Formulations for Thermodynamics and Conservation Principles in Earth System Models (ESMs)
Reconciling and Improving Formulations for Thermodynamics and Conservation Principles in Earth System Models (ESMs) Open
This paper provides a comprehensive derivation of the total energy equations for the atmospheric components of Earth System Models (ESMs). The assumptions and approximations made in this derivation are motivated and discussed. In particula…
View article: Thermodynamically consistent versions of approximations used in modelling moist air
Thermodynamically consistent versions of approximations used in modelling moist air Open
Some existing approaches to modelling the thermodynamics of moist air make approximations that break thermodynamic consistency , such that the resulting thermodynamics does not obey the first and second laws or has other inconsistencies. R…