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The Global Climate Action Partnership

The Global Climate Action Partnership (GCAP; formerly the Low Emission Development Strategies Global Partnership) is a worldwide network that accelerates ambitious climate goals and advances implementation in Africa, Asia, and Latin America and the Caribbean (LAC). Our strategic approach emphasizes country-driven implementation actions to achieve resilient, just, and inclusive low-emission and net-zero economies. As an incubator for scalable knowledge and solutions, GCAP is leading the way to equitable, climate-resilient, low-emission development.

African Climate Action Partnership↗

Gravitational Physics Research

Gravitational physics research at ISPAE is connected with NASA's Relativity Mission (Gravity Probe B (GP-B)) which will perform a test of Einstein's General Relativity Theory. GP-B will measure the geodetic and motional effect predicted by General Relativity Theory with extremely stable and sensitive gyroscopes in an earth orbiting satellite. Both effects cause a very small precession of the gyroscope spin axis. The goal of the GP-B experiment is the measurement of the gyroscope precession with very high precision. GP-B is being developed by a team at Stanford University and is scheduled for launch in the year 2001. The related UAH research is a collaboration with Stanford University and MSFC. This research is focussed primarily on the error analysis and data reduction methods of the experiment but includes other topics concerned with experiment systems and their performance affecting the science measurements. The hydrogen maser is the most accurate and stable clock available. It will be used in future gravitational physics missions to measure relativistic effects such as the second order Doppler effect. The HMC experiment, currently under development at the Smithsonian Astrophysical Observatory (SAO), will test the performance and capability of the hydrogen maser clock for gravitational physics measurements. UAH in collaboration with the SAO science team will study methods to evaluate the behavior and performance of the HMC. The GP-B data analysis developed by the Stanford group involves complicated mathematical operations. This situation led to the idea to investigate alternate and possibly simpler mathematical procedures to extract the GP-B measurements form the data stream. Comparison of different methods would increase the confidence in the selected scheme.

Wu, S. T.↗

Gravity Probe B: Testing Einstein with Gyroscopes

Some 40 years in the making, NASA' s historic Gravity Probe B (GP-B) mission is scheduled to launch aboard a Delta II in 2003. GP-B will test two extraordinary predictions from Einstein's General Relativity: geodetic precession and the Lense-Thirring effect (frame-dragging). Employing tiny, ultra-precise gyroscopes, GP-B features a measurement accuracy of 0.5 milli-arc-seconds per year. The extraordinary measurement precision is made possible by a host of breakthrough technologies, including electro-statically suspended, super-conducting quartz gyroscopes; virtual elimination of magnetic flux; a solid quartz star tracking telescope; helium microthrusters for drag-free control of the spacecraft; and a 2400 liter superfluid helium dewar. This paper will provide an overview of the science, key technologies, flight hardware, integration and test, and flight operations of the GP-B space vehicle. It will also examine some of the technical management challenges of a large-scale, technology-driven, Principal Investigator-led mission.

Geveden, Rex D.↗

Gravity Probe B: Testing Einstein with Gyroscopes

Some 40 years in the making, NASA s historic Gravity Probe B (GP-B) mission is scheduled to launch aboard a Delta I1 in 2003. GP-B will test two extraordinary predictions from Einstein s General Relativity: geodetic precession and the Lense-Thirring effect (frame-dragging). Employing tiny, ultra-precise gyroscopes, GP-B features a measurement accuracy of 0.5 milli-arc-seconds per year. The extraordinary measurement precision is made possible by a host of breakthrough technologies, including electro-statically suspended, super-conducting quartz gyroscopes; virtual elimination of magnetic flux; a solid quartz star- tracking telescope; helium microthrusters for drag-free control of the spacecraft; and a 2400 liter superfluid helium dewar. This paper will provide an overview of the science, key technologies, flight hardware, integration and test, and flight operations of the GP-B space vehicle. It will also examine some of the technical management challenges of a large-scale, technology-driven, Principal Investigator-led mission.

Geveden, Rex D.↗

$\text{GPLaSDI}$: Gaussian Process-based interpretable Latent Space Dynamics Identification through deep autoencoder

Numerically solving partial differential equations (PDEs) can be challenging and computationally expensive. This has led to the development of reduced-order models (ROMs) that are accurate but faster than full order models (FOMs). Recently, machine learning advances have enabled the creation of non-linear projection methods, such as Latent Space Dynamics Identification (LaSDI). LaSDI maps full-order PDE solutions to a latent space using autoencoders and learns the system of ODEs governing the latent space dynamics. By interpolating and solving the ODE system in the reduced latent space, fast and accurate ROM predictions can be made by feeding the predicted latent space dynamics into the decoder. In this paper, we introduce GPLaSDI, a novel LaSDI-based framework that relies on Gaussian process (GP) for latent space ODE interpolations. Using GPs offers two significant advantages. First, it enables the quantification of uncertainty over the ROM predictions. Second, leveraging this prediction uncertainty allows for efficient adaptive training through a greedy selection of additional training data points. This approach does not require prior knowledge of the underlying PDEs. Consequently, GPLaSDI is inherently non-intrusive and can be applied to problems without a known PDE or its residual. Here we demonstrate the effectiveness of our approach on the Burgers equation, Vlasov equation for plasma physics, and a rising thermal bubble problem. Our proposed method achieves between 200 and 100,000 times speed-up, with up to 7% relative error.

97 MATHEMATICS AND COMPUTING↗