Generative Adversarial Networks and Mixture Density Networks-Based Inverse Modeling for Microstructural Materials Design. [PDF]
Integr Mater Manuf Innov, 2022 There are two broad modeling paradigms in scientific applications: forward and inverse. While forward modeling estimates the observations based on known causes, inverse modeling attempts to infer the causes given the observations.
Mao Y +6 more
europepmc +2 more sources
Patient-Specific Inverse Modeling of In Vivo Cardiovascular Mechanics with Medical Image-Derived Kinematics as Input Data: Concepts, Methods, and Applications. [PDF]
Appl Sci (Basel), 2022 Inverse modeling approaches in cardiovascular medicine are a collection of methodologies that can provide non-invasive patient-specific estimations of tissue properties, mechanical loads, and other mechanics-based risk factors using medical imaging as ...
Bracamonte JH +4 more
europepmc +2 more sources
Closing the Mediterranean Marine Floating Plastic Mass Budget: Inverse Modeling of Sources and Sinks. [PDF]
Environ Sci Technol, 2020 Estimates of plastic inputs into the ocean are orders of magnitude larger than what is found in the surface waters. This can be due to discrepancies in the sources of plastic released into the ocean but can also be explained by the fact that it is not ...
Kaandorp MLA +2 more
europepmc +2 more sources
Inverse Modeling for MEG/EEG data [PDF]
, 2017 We provide an overview of the state-of-the-art for mathematical methods that are used to reconstruct brain activity from neurophysiological data. After a brief introduction on the mathematics of the forward problem, we discuss standard and recently ...
A Doucet +40 more
core +4 more sources
Universal inverse modeling of point spread functions for SMLM localization and microscope characterization. [PDF]
Nat MethodsLiu S +11 more
europepmc +2 more sources
Inverse modeling of nonisothermal multiphase poromechanics using physics-informed neural networks [PDF]
Journal of Computational Physics, 2022 We propose a solution strategy for parameter identification in multiphase thermo-hydro-mechanical (THM) processes in porous media using physics-informed neural networks (PINNs).
Daniel Amini, E. Haghighat, R. Juanes
semanticscholar +1 more source
A Machine Learning Framework for Real-time Inverse Modeling and Multi-objective Process Optimization of Composites for Active Manufacturing Control [PDF]
arXiv.org, 2021 For manufacturing of aerospace composites, several parts may be processed simultaneously using convective heating in an autoclave. Due to uncertainties including tool placement, convective Boundary Conditions (BCs) vary in each run.
K. D. Humfeld +4 more
semanticscholar +1 more source
Gait Event Prediction Using Surface Electromyography in Parkinsonian Patients
Bioengineering, 2023 Gait disturbances are common manifestations of Parkinson’s disease (PD), with unmet therapeutic needs. Inertial measurement units (IMUs) are capable of monitoring gait, but they lack neurophysiological information that may be crucial for studying gait ...
Stefan Haufe +3 more
doaj +1 more source
Gait Initiation Impairment in Patients with Parkinson’s Disease and Freezing of Gait
Bioengineering, 2022 Freezing of gait (FOG) is a sudden episodic inability to produce effective stepping despite the intention to walk. It typically occurs during gait initiation (GI) or modulation and may lead to falls.
Chiara Palmisano +5 more
doaj +1 more source
Data-Driven Inverse Problem for Optimizing the Induction Hardening Process of C45 Spur-Gear
Metals, 2023 Inverse problems can be challenging and interesting to study in the context of metallurgical processes. This work aims to carry out a method for inverse modeling for simultaneous double-frequency induction hardening process.
Sevan Garois +2 more
doaj +1 more source