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Periodic material or crystal property prediction using machine learning has grown popular in recent years as it provides a computationally efficient replacement for classical simulation methods. A crucial first step for any of these…
Focal plane wavefront sensing is an appealing technique to cophase multiple aperture telescopes. Phase diversity, operable with any aperture configuration or source extension, generally suffers from high computing load. In this Letter, we…
We present a data-driven, differentiable neural network model designed to learn the temperature field, its gradient, and the cooling rate, while implicitly representing the melt pool boundary as a level set in laser powder bed fusion. The…
In this paper, we introduce the proper latent decomposition (PLD) as a generalization of the proper orthogonal decomposition (POD) on manifolds. PLD is a nonlinear reduced-order modeling technique for compressing high-dimensional data into…
In this two-part article, we evaluate the utility and the generalizability of the Dynamic Mode Decomposition (DMD) algorithm for data-driven analysis and reduced-order modelling of plasma dynamics in cross-field ExB configurations. The DMD…
Recent advances in acquisition equipment is providing experiments with growing amounts of precise yet affordable sensors. At the same time an improved computational power, coming from new hardware resources (GPU, FPGA, ACAP), has been made…
Traditional dataset distillation primarily focuses on image representation while often overlooking the important role of labels. In this study, we introduce Label-Augmented Dataset Distillation (LADD), a new dataset distillation framework…
Achieving high-precision light manipulation is crucial for delivering information through complex media with high fidelity. However, existing spatial light modulation devices face a fundamental tradeoff between speed and accuracy. Digital…
Dissipative Particle Dynamics (DPD) is a popular simulation model for investigating hydrodynamic behavior of systems with non-negligible equilibrium thermal fluctuations. DPD employs soft core repulsive interactions between the system…
The Madison AWAKE Prototype (MAP) is a high-power, high-density helicon plasma experiment. The project's main goal is to develop a scalable plasma source for use in a beam-driven plasma wakefield accelerator as part of the AWAKE project. We…
The linear threshold condition of the mirror mode including weak drift and finite Larmor radius effects is analysed and shown that it can be given to arbitrary precision in closed analytical form. Combined with observations of mirror modes…
Plasma impedance probes (PIPs) are a type of RF probe that primarily measure electron density. This work introduces two advancements: a streamlined analytical model for interpreting PIP-monopole measurements and techniques for achieving…
Large Language Models (LLMs) have exhibited impressive capabilities in various tasks, yet their vast parameter sizes restrict their applicability in resource-constrained settings. Knowledge distillation (KD) offers a viable solution by…
This work aims to demonstrate that two arrays of Lumped Element Kinetic Inductance Detectors (LEKIDs), when employed in filled array configuration and separated by an external linear polarizer oriented at 45 degrees, can achieve the…
High intensity laser-plasma interactions produce a wide array of energetic particles and beams with promising applications. Unfortunately, high repetition rate and high average power requirements for many applications are not satisfied by…
Pulsed-laser deposition (PLD) is one of the most promising techniques for the formation of complex-oxide heterostructures, superlattices, and well-controlled interfaces. The first part of this paper presents a review of several useful…
In this study, we leverage the massive amount of multi-modal on-machine metrology data generated from Laser Powder Directed Energy Deposition (LP-DED) to construct a comprehensive surrogate model of the 3D printing process. By employing…
Training in unsupervised time series anomaly detection is constantly plagued by the discrimination between harmful `anomaly contaminations' and beneficial `hard normal samples'. These two samples exhibit analogous loss behavior that…
Ferroelectric materials with switchable spontaneous polarization underpin non-volatile memories, transistors, sensors, and emerging neuromorphic chips. Their performance and stability are governed by polarization dynamics and domain…
Deterministic Lateral Displacement (DLD) devices are widely used in microfluidics for label-free, size-based separation of particles and cells, with particular promise in isolating circulating tumor cells (CTCs) for early cancer…