arXiv subjects

Alexander Johnston

Publications and source records attributed to Alexander Johnston.

4 recordsLinked to original sources

arXiv2026

Parameter Estimation for Differential Equation Models Using Generalized Profiling: A Computational Tutorial

Parameter estimation connects mathematical models to real-world data and decision making across many scientific and industrial applications. Standard approaches such as maximum likelihood estimation and Markov chain Monte Carlo estimate parameters by repeatedly solving the model, which often requires numerical solutions of differential equation models. In contrast, generalized profiling (also called parameter cascading) focuses directly on the governing differential equation(s), linking the model and data through a penalized likelihood that explicitly measures both the data fit and model fit. Despite several advantages, generalized profiling is relatively rarely used in practice. This tutorial-style article outlines a set of self-directed computational exercises that facilitate skills development in applying generalized profiling to a range of ordinary differential equation models. All calculations can be repeated using reproducible open-source Jupyter notebooks that are available on GitHub.

stat.ME↗

arXiv2024

Efficient inference for differential equation models without numerical solvers

Parameter inference is essential when interpreting observational data using mathematical models. Standard inference methods for differential equation models typically rely on obtaining repeated numerical solutions of the differential equation(s). Recent results have explored how numerical truncation error can have major, detrimental, and sometimes hidden impacts on likelihood-based inference by introducing false local maxima into the log-likelihood function. We present a straightforward approach for inference that eliminates the need for solving the underlying differential equations, thereby completely avoiding the impact of truncation error. Open-access Jupyter notebooks, available on GitHub, allow others to implement this method for a broad class of widely-used models to interpret biological data.

stat.ME↗

arXiv2024

Macroscopic noise amplification by asymmetric dyads in non-Hermitian optical systems for generative diffusion models

A new generation of sensors, hardware random number generators, and quantum and classical signal detectors are exploiting strong responses to external perturbations of system noise. Here, we study noise amplification by asymmetric dyads in freely expanding non-Hermitian optical systems. We show that modifications of the pumping strengths can counteract bias from natural imperfections of the system's hardware, while couplings between dyads lead to systems with non-uniform statistical distributions. Our results suggest that asymmetric non-Hermitian dyads are promising candidates for efficient sensors and ultra-fast random number generators. We propose that the integrated light emission from such asymmetric dyads can be efficiently used for analog all-optical degenerative diffusion models of machine learning to overcome the digital limitations of such models in processing speed and energy consumption.

quant-ph↗

arXiv2020

Artificial polariton molecules

We show that geometrically coupled polariton condensates fabricated in semiconductor devices are versatile systems capable of simulating molecules with given characteristics. In particular, we consider oscillatory and stationary symmetric and asymmetric states in polariton dimers, trimers, and tetrads and their luminosity in real and Fourier space. The spectral weights of oscillatory states are associated with discrete spectral lines. Their number and separation can be controlled by changing the number and geometry of condensates, reflected by the coupling strengths. We also show that asymmetric stationary states combine discrete and continuous degrees of freedom in one system. The continuous degree of freedom is represented by the phase while the discrete degree of freedom is given by density asymmetry. Our work paves the way to engineer controllable artificial molecules with a range of properties manufactured on demand.

cond-mat.mes-hall↗