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Fluid turbulence is characterized by strong coupling across a broad range of scales. Furthermore, besides the usual local cascades, such coupling may extend to interactions that are non-local in scale-space. As such the computational demands associated with explicitly resolving the full set of scales and their interactions, as in the Direct Numerical Simulation (DNS) of the Navier-Stokes equations, in most problems of practical interest are so high that reduced modeling of scales and interactions is required before further progress can be made. While popular reduced models are typically based on phenomenological modeling of relevant turbulent processes, recent advances in machine learning techniques have energized efforts to further improve the accuracy of such reduced models. In contrast to such efforts that seek to improve an existing turbulence model, we propose a machine learning (ML) methodology that captures, de novo, underlying turbulence phenomenology without a pre-specified model form. To illustrate the approach, we consider transient modeling of the dissipation of turbulent kinetic energy-a fundamental turbulent process that is central to a wide range of turbulence models-using a Neural ODE approach. After presenting details of the methodology, we show that this approach out-performs state-of-the-art approaches.

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Symbiotic stars (SySts) are long-period interacting binaries composed of a hot compact star, an evolved giant star, and a tangled network of gas and dust nebulae. They represent unique laboratories for studying a variety of important astrophysical problems, and have also been proposed as possible progenitors of SNIa. Presently, we know 257 SySts in the Milky Way and 69 in external galaxies. However, these numbers are still in striking contrast with the predicted population of SySts in our Galaxy. Because of other astrophysical sources that mimic SySt colors, no photometric diagnostic tool has so far demonstrated the power to unambiguously identify a SySt, thus making the recourse to costly spectroscopic follow-up still inescapable. In this paper we present the concept, commissioning, and science verification phases, as well as the first scientific results, of RAMSES II -a Gemini Observatory Instrument Upgrade Project that has provided each GMOS instrument at both Gemini telescopes with a set of narrow-band filters centered on the Raman OVI 6830Å band. Continuum-subtracted images using these new filters clearly revealed known SySts with a range of Raman OVI line strengths, even in crowded fields. RAMSES II observations also produced the first detection of Raman OVI emission from the SySt LMC 1 and confirmed Hen 3-1768 as a new SySt -the first photometric confirmation of a SySt. Via Raman OVI narrow-band imaging, RAMSES II provides the astronomical community with the first purely photometric tool for hunting SySts in the local Universe.