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Scientific Machine Learning is a new class of approaches that integrate physical knowledge and mechanistic models with data-driven techniques for uncovering governing equations of complex processes. Among the available approaches, Universal…
These four lectures constitute a gentle introduction to what may lie beyond the standard model of quarks and leptons interacting through $SU(3)_c \otimes SU(2)_L \otimes U(1)_Y$ gauge bosons, prepared for an audience of graduate students in…
We review the new possibilities offered by the reaction dynamics of asymmetric heavy ion collisions, using stable and unstable beams. We show that it represents a rather unique tool to probe regions of highly Asymmetric Nuclear Matter…
Neural networks (NNs) often assign high confidence to their predictions, even for points far out-of-distribution, making uncertainty quantification (UQ) a challenge. When they are employed to model interatomic potentials in materials…
Searches for intrinsic electric dipole moments (EDMs) of nucleons, atoms and molecules are precision flavor-diagonal probes of new $CP$-odd physics, as motivated by the need to explain the matter-antimatter asymmetry in the universe. We…
We discuss the mechanism for electroweak symmetry breaking in supersymmetric versions of the standard model. After briefly reviewing the possible sources of supersymmetry breaking, we show how the required pattern of symmetry breaking can…
Searches for the permanent electric dipole moments (EDMs) of molecules, atoms, nucleons and nuclei provide powerful probes of CP violation both within and beyond the Standard Model (BSM). The interpretation of experimental EDM limits…
Deep learning-based numerical schemes for solving high-dimensional backward stochastic differential equations (BSDEs) have recently raised plenty of scientific interest. While they enable numerical methods to approximate very…
Essentials of the scientific discovery process have remained largely unchanged for centuries: systematic human observation of natural phenomena is used to form hypotheses that, when validated through experimentation, are generalized into…
The accessibility of spatially distributed data, enabled by affordable sensors, field, and numerical experiments, has facilitated the development of data-driven solutions for scientific problems, including climate change, weather…
The most important hint of physics beyond the Standard Model (SM) from the 1995 precision electroweak data is that the most precisely measured quantities, the total, leptonic and hadronic decay widths of the $Z$ and the effective weak…
An accurate description of information is relevant for a range of problems in atomistic machine learning (ML), such as crafting training sets, performing uncertainty quantification (UQ), or extracting physical insights from large datasets.…
This work demonstrates the ability to produce readily interpretable statistical metrics for model fit, fixed effects covariance coefficients, and prediction confidence. Importantly, this work compares 4 suitable and commonly applied…
Understanding nuclear effects is essential for improving the sensitivity of neutrino oscillation measurements. Validating nuclear models solely through neutrino scattering data is challenging due to limited statistics and the broad energy…
We study the implications for electroweak baryogenesis (EWB) within the minimal supersymmetric Standard Model (MSSM) of present and future searches for the permanent electric dipole moment (EDM) of the electron, for neutralino dark matter,…
We show that a discrete exchange symmetry can give rise to realistic dark matter candidates in models with warped extra dimensions. We show how to realize our construction in a variety of models with warped extra dimensions and study in…
Understanding the source of the universe's asymmetry between matter and antimatter is one of the major open questions in particle physics. In this work, the sensitivity of novel machine-learning-based inference techniques to CP-odd and…
A nonuniversal abelian extension $\mathrm{U(1)}_{X}$ free from chiral anomalies is introduced into the Standard Model (SM), in order to evaluate its suitability in addressing the fermion mass hierarchy (FMH) by using seesaw mechanisms…
We discuss some of the signatures associated with extensions of the Standard Model related to the neutrino and electroweak symmetry breaking sectors, with and without supersymmetry. The topics include a basic discussion of the theory of…
Many problems in engineering and sciences require the solution of large scale optimization constrained by partial differential equations (PDEs). Though PDE-constrained optimization is itself challenging, most applications pose additional…