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A physics-constrained deep learning surrogate that predicts the exponential ‘avalanche’ growth rate of runaway electrons (REs) for a plasma containing partially ionized impurities is developed. Specifically, a physics-informed neural network (PINN) that learns the adjoint of the relativistic Fokker–Planck equation in steady-state is derived, enabling a rapid surrogate of the RE avalanche for a broad range of plasma parameters, motivating a path towards an machine learning-accelerated integrated description of a tokamak disruption. A steady-state power balance equation together with atomic physics data is embedded directly into the PINN, thus limiting the PINN to train across physically consistent temperatures and charge state distributions. This restricted training domain enables accurate predictions of the PINN while drastically reducing the computational cost of training the model. In addition, a novel closure for the relativistic electron population used when evaluating the secondary source of REs is developed that enables improved accuracy compared to a Rosenbluth–Putvinski source. The avalanche surrogate is verified against Monte Carlo simulations, where it is shown to accurately predict the RE avalanche growth rate across a broad range of plasma parameters encompassing distinct tokamak disruption scenarios.

An adjoint formulation of energetic particle confinement in axisymmetric tokamak geometry is derived and evaluated using a physics-informed neural network (PINN). The PINN estimates the mean escape time of energetic ions by solving an inhomogeneous adjoint of the drift kinetic equation with a Lorentz collision operator, yielding predictions of fast ion loss in tokamak geometry due to direct ion orbit loss and collisional transport. To our knowledge, this is the first time a PINN has been used to solve the drift kinetic equation in tokamak geometry, a challenging problem due to the large time scale separation between the rapid transit time of energetic ions and their slow collisional time scale. It is shown that a careful and intentional design of a PINN is able to learn the mean escape time across the majority of the plasma volume, suggesting a path toward constructing a rapid surrogate for use within a broader optimization framework.

An adjoint formulation of energetic particle confinement in axisymmetric geometry is derived and evaluated using a Physics-Informed Neural Network (PINN). The PINN estimates the escape time of energetic ions by solving an inhomogeneous adjoint of the drift kinetic equation with a Lorentz collision operator, yielding predictions of the escape time of fast ions in tokamak geometry due to direct ion orbit loss and collisional transport. This is the first time a PINN has been used to solve the drift kinetic equation in tokamak geometry, a challenging problem due to the large time scale separation present between the rapid transit time of energetic ions, and their slow collision time scale. It is shown that a careful and intentional design of a PINN is able to learn the escape time for the majority of the geometry considered, suggesting a path toward constructing a rapid surrogate for use in a broader optimization framework.

A physics-constrained deep learning surrogate that predicts the exponential ``avalanche'' growth rate of runaway electrons (REs) for a plasma containing partially ionized impurities is developed. Specifically, a physics-informed neural network (PINN) that learns the adjoint of the relativistic Fokker-Planck equation in steady-state is derived, enabling a rapid surrogate of the RE avalanche for a broad range of plasma parameters, motivating a path towards an ML-accelerated integrated description of a tokamak disruption. A steady-state power balance equation together with atomic physics data is embedded directly into the PINN, thus limiting the PINN to train across physically consistent temperatures and charge state distributions. This restricted training domain enables accurate predictions of the PINN while drastically reducing the computational cost of training the model. In addition, a novel closure for the relativistic electron population used when evaluating the secondary source of REs is developed that enables improved accuracy compared to a Rosenbluth-Putvinski source. The avalanche surrogate is verified against Monte Carlo simulations, where it is shown to accurately predict the RE avalanche growth rate across a broad range of plasma parameters encompassing distinct tokamak disruption scenarios.

A physics-informed neural network (PINN) is developed, for the first time, to learn the time-dependent quasi-static magnetohydrodynamic (MHD) equations in axisymmetric tokamak geometry, without any experimental or synthetic data. The initial study considered an ITER-like tokamak and found that a PINN, after careful treatment, was capable of learning the solution to the MHD system and predict a vertically displacing plasma, where general agreement with ground truth simulation was observed. The proof-of-principle demonstration highlights the potential of physics-constrained deep learning to learn complex plasma behavior.

A surrogate model of the runaway electron avalanche growth rate in a magnetic fusion plasma is developed. This is accomplished by employing a physics-informed neural network (PINN) to learn the parametric solution of the adjoint to the relativistic Fokker-Planck equation. The resulting PINN is able to evaluate the runaway probability function across a broad range of parameters in the absence of any synthetic or experimental data. This surrogate of the adjoint relativistic Fokker-Planck equation is then used to infer the avalanche growth rate as a function of the electric field, synchrotron radiation and effective charge. Predictions of the avalanche PINN are compared against first principle calculations of the avalanche growth rate with excellent agreement observed across a broad range of parameters.

Contemporary bioinformatic and chemoinformatic capabilities hold promise to reshape knowledge management, analysis and interpretation of data in natural products research. Currently, reliance on a disparate set of non-standardized, insular, and specialized databases presents a series of challenges for data access, both within the discipline and for integration and interoperability between related fields. The fundamental elements of exchange are referenced structure-organism pairs that establish relationships between distinct molecular structures and the living organisms from which they were identified. Consolidating and sharing such information via an open platform has strong transformative potential for natural products research and beyond. This is the ultimate goal of the newly established LOTUS initiative, which has now completed the first steps toward the harmonization, curation, validation and open dissemination of 750,000+ referenced structure-organism pairs. LOTUS data is hosted on Wikidata and regularly mirrored on https://lotus.naturalproducts.net . Data sharing within the Wikidata framework broadens data access and interoperability, opening new possibilities for community curation and evolving publication models. Furthermore, embedding LOTUS data into the vast Wikidata knowledge graph will facilitate new biological and chemical insights. The LOTUS initiative represents an important advancement in the design and deployment of a comprehensive and collaborative natural products knowledge base.

The conserved MICOS complex functions as a primary determinant of mitochondrial inner membrane structure. We address the organization and functional roles of MICOS and identify two independent MICOS subcomplexes: Mic27/Mic10/Mic12, whose assembly is dependent on respiratory complexes and the mitochondrial lipid cardiolipin, and Mic60/Mic19, which assembles independent of these factors. Our data suggest that MICOS subcomplexes independently localize to cristae junctions and are connected via Mic19, which functions to regulate subcomplex distribution, and thus, potentially also cristae junction copy number. MICOS subunits have non-redundant functions as the absence of both MICOS subcomplexes results in more severe morphological and respiratory growth defects than deletion of single MICOS subunits or subcomplexes. Mitochondrial defects resulting from MICOS loss are caused by misdistribution of respiratory complexes in the inner membrane. Together, our data are consistent with a model where MICOS, mitochondrial lipids and respiratory complexes coordinately build a functional and correctly shaped mitochondrial inner membrane. Structures called mitochondria provide energy that cells need to live and grow. To do this, mitochondria convert energy stored within sugars and other carbon-rich compounds into the energy currency of cells, a molecule called adenosine triphosphate (called ATP for short). Defective mitochondria can cause cells to starve and also cause severe human diseases. A double membrane surrounds each mitochondrion. The outer membrane allows proteins and other substances to enter, while the inner membrane is elaborately folded and contains several groups of proteins—or complexes—including the respiratory complexes that generate ATP. Proper inner membrane folding is critically important. The membrane folds are held in place by structures called cristae junctions, which may also help to restrict proteins to particular areas of the inner membrane. A large inner membrane complex of proteins known as MICOS is important for organizing the inner membrane into folds, although exactly how it does so is not fully understood. MICOS consists of at least six different proteins, most of which are found across yeast and animal species. Friedman et al. have now analyzed how the MICOS complex assembles on the inner membrane in yeast cells using a combination of fluorescence and electron microscopy, proteomics and biochemistry. This revealed that in yeast, MICOS is made up of two independent sub-complexes bridged together by a protein called Mic19, which additional experiments suggest controls the number and positions of the cristae junctions that hold the folds of the inner membrane in place. As part of the approach to understand MICOS complex organization, Friedman et al. removed the six MICOS proteins from yeast cells. Inside these cells, the inner mitochondrial membrane was misfolded. Furthermore, the respiratory complexes did not work normally and as a consequence the cells were unable to grow normally, suggesting that the correct distribution of respiratory complexes in the inner membrane is important for ATP production and depends on MICOS. These results indicate that MICOS stabilizes the structure of the inner membrane and organizes it into an efficient energy-generating machine. In many human mitochondrial diseases, the inner membrane of mitochondria folds incorrectly, in similar ways to the misfolding seen in the yeast cells that did not contain the MICOS complex. Therefore, the MICOS complex may also influence how these diseases develop.

This paper develops tools and techniques to analyze the determinants of factor allocation and factor prices in economies with a large number of goods and factors. The main results of our paper characterize sufficient conditions for robust monotone comparative statics predictions in a Roy-like assignment model. These general results are then used to generate new insights about the consequences of globalization.

We report the discovery of the Oceanus moving group, a ≈500 Myr old group with 50 members and candidate members at distances 2–50 pc from the Sun, using an unsupervised clustering analysis of nearby stars with Gaia DR3 data. This new moving group includes the nearest brown dwarf WISE J104915.57–531906.1 AB (Luhman 16 AB) at a distance of 2 pc, which was previously suspected to be young (600–800 Myr) based on a comparison of its dynamical mass measurements with brown dwarf evolutionary models. We use empirical color–magnitude sequences, stellar activity, and gyrochronology to determine that this new group is roughly coeval with the Coma Ber open cluster, with an isochronal age of 510 ± 95 Myr. This newly discovered group will be useful to refine the age and chemical composition of Luhman 16 AB, which is already one of the best substellar benchmarks known to date. Furthermore, the Oceanus moving group is one of the nearest young moving groups identified to date, making it a valuable laboratory for the study of exoplanets and substellar members, with eight brown dwarf candidate members already identified here.