Training a neural network to predict dynamics it has never seen.

Neural networks have proven to be remarkably successful for a wide range of complicated tasks, from image recognition and object detection to speech recognition and machine translation. One of their successes lies in their ability to predict future dynamics given a suitable training data set. Previous studies have shown how echo state networks (ESNs), a type of recurrent neural networks, can successfully predict both short-term and long-term dynamics of even chaotic systems.

Dynamics of phase slips in systems with time-periodic modulation.

The Adler equation with time-periodic frequency modulation is studied. A series of resonances between the period of the frequency modulation and the time scale for the generation of a phase slip is identified. The resulting parameter space structure is determined using a combination of numerical continuation, time simulations, and asymptotic methods.

Reduced description of exact coherent states in parallel shear flows.

A reduced description of exact coherent structures in the transition regime of plane parallel shear flows is developed, based on the Reynolds number scaling of streamwise-averaged (mean) and streamwise-varying (fluctuation) velocities observed in numerical simulations. The resulting system is characterized by an effective unit Reynolds number mean equation coupled to linear equations for the fluctuations, regularized by formally higher-order diffusion. Stationary coherent states are computed by solving the resulting equations simultaneously using a robust numerical algorithm developed for this purpose.

Localized states in periodically forced systems.

The theory of stationary spatially localized patterns in dissipative systems driven by time-independent forcing is well developed. With time-periodic forcing, related but time-dependent structures may result. These may consist of breathing localized patterns, or states that grow for part of the cycle via nucleation of new wavelengths of the pattern followed by wavelength annihilation during the remainder of the cycle.

Spatial localization in heterogeneous systems.

We study spatial localization in the generalized Swift-Hohenberg equation with either quadratic-cubic or cubic-quintic nonlinearity subject to spatially heterogeneous forcing. Different types of forcing (sinusoidal or Gaussian) with different spatial scales are considered and the corresponding localized snaking structures are computed. The results indicate that spatial heterogeneity exerts a significant influence on the location of spatially localized structures in both parameter space and physical space, and on their stability properties.

Electrolyte stability in a nanochannel with charge regulation.

The stability of an electrolyte confined in one dimension between two solid surfaces is analyzed theoretically in the case where overlapping double layers produce nontrivial interactions. Within the Poisson-Boltzmann-Nernst-Planck description of the electrostatic interaction and transport of electrical charges, the presence of Stern layers can enrich the set of possible solutions. Our analytical and numerical study of the stability properties of the trivial state of this system identified an instability to a new antisymmetric state.

Socio-demographic predictors of high support needs in newly diagnosed breast cancer patients.

This study aimed to identify high support needs and their socio-demographic predictors to improve supportive care for newly diagnosed breast cancer patients. A cross-sectional study measured patients' needs and unsatisfied support needs by the supportive care needs survey (SCNS-34), administered after surgery, chemotherapy or radiotherapy. Socio-demographic, disease and treatment characteristics completed data collection.