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With the rise of neural networks in various domains, multi-task learning (MTL) gained significant relevance. A key challenge in MTL is balancing individual task losses during neural network training to improve performance and efficiency…

A new dynamic latent space eigenmodel (LSM) is proposed for weighted temporal networks. The model accommodates integer-valued weights, excess of zeros, time-varying node positions (features), and time-varying network sparsity. The latent…

We present model independent reconstructions of quintessence and the Swampland conjectures (SC) using both Machine Learning (ML) and cosmography. In particular, we demonstrate how the synergies between theoretical analyses and ML can…

One of the biggest challenges in cosmology, the Hubble Tension, requires independent measurements of $H_0$, and strong lensing with time-delay cosmography is a promising avenue. The inclusion of spatially resolved kinematic data helps break…

Datasets in engineering applications are often limited and contaminated, mainly due to unavoidable measurement noise and signal distortion. Thus, using conventional data-driven approaches to build a reliable discriminative model, and…

The Hubble Tension is a well-known issue in modern cosmology that refers to the apparent disagreement in inferences of the Hubble constant $H_0$ as found through low-redshift observations and those derived from the $\Lambda$CDM model…

Time series prediction is challenging due to our limited understanding of the underlying dynamics. Conventional models such as ARIMA and Holt's linear trend model experience difficulty in identifying nonlinear patterns in time series. In…

Multi-task learning (MTL) considers learning a joint model for multiple tasks by optimizing a convex combination of all task losses. To solve the optimization problem, existing methods use an adaptive weight updating scheme, where task…

The dominant uncertainty in the current measurement of the Hubble constant ($H_0$) with strong gravitational lensing time delays is attributed to uncertainties in the mass profiles of the main deflector galaxies. Strongly lensed supernovae…

In recent years, the modified theory of gravity known as $f(Q)$ gravity has drawn interest as a potential alternative to general relativity. According to this theory, the gravitational force is determined by a function of the so-called…

Using a correspondence between the f(T)-gravity with the power-law entropy corrected version of the holographic dark energy model, we reconstruct the holographic f(T)-gravity model with power-law entropy correction. We fit the model…

Fifth forces are ubiquitous in modified theories of gravity. In this paper, we analyze their effect on the Cepheid-calibrated cosmic distance ladder, specifically with respect to the inferred value of the Hubble constant ($H_0$). We…

We search for viable f(R) theories of gravity, making use of the equivalence between such theories and scalar-tensor gravity. We find that models can be made consistent with solar system constraints either by giving the scalar a high mass…

Machine learning (ML) techniques applied to quantum many-body physics have emerged as a new research field. While the numerical power of this approach is undeniable, the most expressive ML algorithms, such as neural networks, are black…

Alterations of the gravity Lagrangian introduced in modified torsion gravity theories---also referred to as $f(T)$ gravity---allows for an accelerated expansion in a matter dominated Universe. In this framework, the cosmic speed up is…

Science-based simulation tools such as Finite Element (FE) models are routinely used in scientific and engineering applications. While their success is strongly dependent on our understanding of underlying governing physical laws, they…

Strongly lensed supernovae are a promising new probe to obtain independent measurements of the Hubble constant (${H_0}$). In this work, we employ simulated gravitationally lensed Type Ia supernovae (glSNe Ia) to train our machine learning…

Decaying dark matter models generically modify the equation of state around the time of dark matter decay, and this in turn modifies the expansion rate of the Universe through the Friedmann equation. Thus, a priori, these models could solve…

We investigate the application of hybrid quantum tensor networks to aeroelastic problems, harnessing the power of Quantum Machine Learning (QML). By combining tensor networks with variational quantum circuits, we demonstrate the potential…

Bayesian reinforcement learning (BRL) is a method that merges principles from Bayesian statistics and reinforcement learning to make optimal decisions in uncertain environments. As a model-based RL method, it has two key components: (1)…