Machine Learning

Characterization of partial wetting by CMAS droplets using multiphase many-body dissipative particle dynamics and data-driven discovery based on PINNs
The molten sand that is a mixture of calcia, magnesia, alumina and silicate, known as CMAS, is characterized by its high viscosity, …
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Machine-learning-based data-driven discovery of nonlinear phase-field dynamics
One of the main questions regarding complex systems at large scales concerns the effective interactions and driving forces that emerge …
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On the parameter combinations that matter and on those that do not: data-driven studies of parameter (non)identifiability
We present a data-driven approach to characterizing nonidentifiability of a model’s parameters and illustrate it through dynamic as …
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MAVE-NN: learning genotype-phenotype maps from multiplex assays of variant effect
Multiplex assays of variant effect (MAVEs) are a family of methods that includes deep mutational scanning experiments on proteins and …
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Periodicity Scoring of Time Series Encodes Dynamical Behavior of the Tumor Suppressor p53
In this paper we analyze the dynamical behavior of the tumor suppressor protein p53, an essential player in the cellular stress …
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Emergent spaces for coupled oscillators
Systems of coupled dynamical units (e.g., oscillators or neurons) are known to exhibit complex, emergent behaviors that may be …
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Manifold learning for organizing unstructured sets of process observations
Data mining is routinely used to organize ensembles of short temporal observations so as to reconstruct useful, low-dimensional …
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Coarse-scale PDEs from fine-scale observations via machine learning
Complex spatiotemporal dynamics of physicochemical processes are often modeled at a microscopic level (through, e.g., atomistic, …
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Manifold learning for parameter reduction
Large scale dynamical systems (e.g. many nonlinear coupled differential equations) can often be summarized in terms of only a few state …
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