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1. Forward and Inverse Modeling of Ice Sheet Flow Using Physics‐Informed Neural Networks: Application to Helheim Glacier, Greenland

   https://doi.org/10.1029/2024JH000169  

   Cheng, Gong; Morlighem, Mathieu; Francis, Sade (September 2024, Journal of Geophysical Research: Machine Learning and Computation)

   Abstract Predicting the future contribution of the ice sheets to sea level rise over the next decades presents several challenges due to a poor understanding of critical boundary conditions, such as basal sliding. Traditional numerical models often rely on data assimilation methods to infer spatially variable friction coefficients by solving an inverse problem, given an empirical friction law. However, these approaches are not versatile, as they sometimes demand extensive code development efforts when integrating new physics into the model. Furthermore, this approach makes it difficult to handle sparse data effectively. To tackle these challenges, we use the Physics‐Informed Neural Networks (PINNs) to seamlessly integrate observational data and governing equations of ice flow into a unified loss function, facilitating the solution of both forward and inverse problems within the same framework. We illustrate the versatility of this approach by applying the framework to two‐dimensional problems on the Helheim Glacier in southeast Greenland. By systematically concealing one variable (e.g., ice speed, ice thickness, etc.), we demonstrate the ability of PINNs to accurately reconstruct hidden information. Furthermore, we extend this application to address a challenging mixed inversion problem. We show how PINNs are capable of inferring the basal friction coefficient while simultaneously filling gaps in the sparsely observed ice thickness. This unified framework offers a promising avenue to enhance the predictive capabilities of ice sheet models, reducing uncertainties, and advancing our understanding of poorly constrained physical processes. 

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2. Prospects for thermalization of microwave-shielded ultracold molecules

   https://doi.org/10.1103/PhysRevResearch.6.L022033  

   Wang, Reuben_R W; Bohn, John L (May 2024, Physical Review Research)

   Toward more efficient schemes for achieving deeply degenerate molecular Fermi gases, we study anisotropic thermalization in dilute gases of microwave shielded polar molecular fermions. For collision energies above the threshold regime, we find that thermalization is suppressed due to a strong preference for forward scattering and a reduction in total cross section with energy, significantly reducing the efficiency of evaporative cooling. We perform close-coupling calculations on the effective potential energy surface derived by Deng [] to obtain accurate two-body elastic differential cross sections across a range of collision energies. We use Gaussian process regression to obtain a global representation of the differential cross section over a wide range of collision angles and energies. The route to equilibrium is then analyzed with cross-dimensional rethermalization experiments, quantified by a measure of collisional efficiency toward achieving thermalization. Published by the American Physical Society2024 

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3. Precision cross-sections for advancing cosmic-ray physics and other applications: a comprehensive programme for the next decade

   Maurin, D; Audouin, L; Berti, E; Coppin, P; Di_Mauro, M; von_Doetinchem, P; Donato, F; Evoli, C; Gennolini, Y; Ghosh, P; et al (March 2025, arXivorg)

   Cosmic-ray physics in the GeV-to-TeV energy range has entered a precision era thanks to recent data from space-based experiments. However, the poor knowledge of nuclear reactions, in particular for the production of antimatter and secondary nuclei, limits the information that can be extracted from these data, such as source properties, transport in the Galaxy and indirect searches for particle dark matter. The Cross-Section for Cosmic Rays at CERN workshop series has addressed the challenges encountered in the interpretation of high-precision cosmic-ray data, with the goal of strengthening emergent synergies and taking advantage of the complementarity and know-how in different communities, from theoretical and experimental astroparticle physics to high-energy and nuclear physics. In this paper, we present the outcomes of the third edition of the workshop that took place in 2024. We present the current state of cosmic-ray experiments and their perspectives, and provide a detailed road map to close the most urgent gaps in cross-section data, in order to efficiently progress on many open physics cases, which are motivated in the paper. Finally, with the aim of being as exhaustive as possible, this report touches several other fields -- such as cosmogenic studies, space radiation protection and hadrontherapy -- where overlapping and specific new cross-section measurements, as well as nuclear code improvement and benchmarking efforts, are also needed. We also briefly highlight further synergies between astroparticle and high-energy physics on the question of cross-sections. 

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4. Statistical (n,$$\gamma $$) cross section model comparison for short-lived nuclei

   https://doi.org/10.1140/epja/s10050-023-00920-0  

   Lewis, R.; Couture, A.; Liddick, S. N.; Spyrou, A.; Bleuel, D. L.; Campo, L. Crespo; Crider, B. P.; Dombos, A. C.; Guttormsen, M.; Kawano, T.; et al (March 2023, The European Physical Journal A)

   Abstract Neutron-capture cross sections of neutron-rich nuclei are calculated using a Hauser–Feshbach model when direct experimental cross sections cannot be obtained. A number of codes to perform these calculations exist, and each makes different assumptions about the underlying nuclear physics. We investigated the systematic uncertainty associated with the choice of Hauser-Feshbach code used to calculate the neutron-capture cross section of a short-lived nucleus. The neutron-capture cross section for$$^{73}\hbox {Zn}$$ ${}^{73}\text{Zn}$(n,$$\gamma $$ $\gamma$)$$^{74}\hbox {Zn}$$ ${}^{74}\text{Zn}$was calculated using three Hauser-Feshbach statistical model codes: TALYS, CoH, and EMPIRE. The calculation was first performed without any changes to the default settings in each code. Then an experimentally obtained nuclear level density (NLD) and$$\gamma $$ $\gamma$-ray strength function ($$\gamma \hbox {SF}$$ $\gamma \text{SF}$) were included. Finally, the nuclear structure information was made consistent across the codes. The neutron-capture cross sections obtained from the three codes are in good agreement after including the experimentally obtained NLD and$$\gamma \hbox {SF}$$ $\gamma \text{SF}$, accounting for differences in the underlying nuclear reaction models, and enforcing consistent approximations for unknown nuclear data. It is possible to use consistent inputs and nuclear physics to reduce the differences in the calculated neutron-capture cross section from different Hauser-Feshbach codes. However, ensuring the treatment of the input of experimental data and other nuclear physics are similar across multiple codes requires a careful investigation. For this reason, more complete documentation of the inputs and physics chosen is important. 

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5. Solving, tracking and stopping streaming linear inverse problems

   https://doi.org/10.1088/1361-6420/ad5583  

   Pritchard, Nathaniel; Patel, Vivak (June 2024, Inverse Problems)

   Abstract In large-scale applications including medical imaging, collocation differential equation solvers, and estimation with differential privacy, the underlying linear inverse problem can be reformulated as a streaming problem. In theory, the streaming problem can be effectively solved using memory-efficient, exponentially-converging streaming solvers. In special cases when the underlying linear inverse problem is finite-dimensional, streaming solvers can periodically evaluate the residual norm at a substantial computational cost. When the underlying system is infinite dimensional, streaming solver can only access noisy estimates of the residual. While such noisy estimates are computationally efficient, they are useful only when their accuracy is known. In this work, we rigorously develop a general family of computationally-practical residual estimators and their uncertainty sets for streaming solvers, and we demonstrate the accuracy of our methods on a number of large-scale linear problems. Thus, we further enable the practical use of streaming solvers for important classes of linear inverse problems. 

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