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Computer simulations are invaluable tools for scientific discovery. However, accurate simulations are often slow to execute, which limits their applicability to extensive parameter exploration, large-scale data analysis, and uncertainty quantification. A promising route to accelerate simulations by building fast emulators with machine learning requires large training datasets, which can be prohibitively expensive to obtain with slow simulations. Here we present a method based on neural architecture search to build accurate emulators even with a limited number of training data. The method successfully emulates simulations in 10 scientific cases including astrophysics, climate science, biogeochemistry, high energy density physics, fusion energy, and seismology, using the same super-architecture, algorithm, and hyperparameters. Our approach also inherently provides emulator uncertainty estimation, adding further confidence in their use. We anticipate this work will accelerate research involving expensive simulations, allow more extensive parameters exploration, and enable new, previously unfeasible computational discovery. UK EPSRC Grant EP/P015794/1 and the Royal Society UK EPSRC (EP/M022331/1 and EP/N014472/1) European Union’s Horizon 2020 (Grant Agreement No. 805162) Natural Environment Research Council (NERC) NE/P013406/1 (A-CURE)

Our understanding of physical systems often depends on our ability to match complex computational modeling with the measured experimental outcomes. However, simulations with large parameter spaces suffer from inverse problem instabilities, where similar simulated outputs can map back to very different sets of input parameters. While of fundamental importance, such instabilities are seldom resolved due to the intractably large number of simulations required to comprehensively explore parameter space. Here, we show how Bayesian inference can be used to address inverse problem instabilities in the interpretation of x-ray emission spectroscopy and inelastic x-ray scattering diagnostics. We find that the extraction of information from measurements on the basis of agreement with simulations alone is unreliable and leads to a significant underestimation of uncertainties. We describe how to statistically quantify the effect of unstable inverse models and describe an approach to experimental design that mitigates its impact.

International audience; Many supernova remnants (SNRs), such as G296.5 + 10.0, exhibit an axisymmetric or barrel shape. Such morphologies have previously been linked to the direction of the Galactic magnetic field (GMF) although this remains uncertain. These SNRs generate magnetohydrodynamic (MHD) shocks in the interstellar medium (ISM), modifying its physical and chemical properties. The ability to study these shocks through observations is difficult due to the small spatial scales involved. In order to answer these questions, we perform a scaled laboratory experiment in which a laser-generated blast wave expands under the influence of a uniform magnetic field. The blast wave exhibits a spheroidal shape, whose major axis is aligned with the magnetic field, in addition to a more continuous shock front. The implications of our results are discussed in the context of astrophysical systems.