The Electromagnetics Full Wave Solvers (P-EM-FDTD) provide accelerated full-wave EM modeling (> 1 billion voxels) on adaptive, inhomogeneous meshes and a unique multi-resolution subgridding algorithm for optimal local mesh refinement. The solvers support dispersive materials, thin layer models, conformal corrections, lumped circuit element modeling, and a rich set of boundary conditions and source types. Furthermore, they offer accurate and efficient skin models for mm-wave applications, and are powerful enough to handle the full complexity of our Virtual Population anatomical models.
Application Areas
EM system modeling and optimization
Biomedical device design and optimization
Antenna and antenna array design, placement optimization, link budgets
Wireless power transfer (WPT) systems
Wireless communication device design
Radar
Impedance, cross-talk, interference
In-to-in and on-to-in body link optimization
Exposure assessment (incident E- / H-fields, iPD, induced fields, SAR, APD, ΔT, ICNIRP and IEEE exposure standard metrics)
MRI system (pTx RF coils, Rx RF coils) coil design
MR exposure safety (including active and passive implants)
RF/MW hyperthermia
RF tumor ablation
Solver Capabilities
Transient, broadband, and harmonic simulations (Time-Domain Solver)
Mulit-GPU acceleration for >1000x speed-up
Multiport simulations
Results from time and frequency domains
Automatic simulation termination
ARMA engine for early time convergence detection
Gridding engine (geometry analysis) for optimized inhomogeneous meshes
Adaptive subgridding algorithm powered by Acceleware
Run-time monitoring
Materials and Properties
Frequency-dependent dielectric and magnetic materials (Debye, Lorentz, Drude, Drude-Lorentz)
Broadband skin model (10–110 GHz): Implements the latest IEC/IEEE-adopted standard for accurate absorbed power density and OTA evaluations
Anisotropic material support for EM FDTD CUDA-accelerated solvers
Sources and Boundary Conditions
User-defined signal sources (pulse, step, saw, custom)
Discrete sources (1D, single edge)
Plane wave and Huygens box sources (total field/scattered field)
Remote and iterative Huygens engines (with backscattering)
Multiport simulations
Lumped elements (R, L, C, predefined series/parallel)
Parametric sources, sensors
ABC, PEC, PMC, periodic boundaries
Analytic boundaries (Mur, Higdon)
UPML and CPML boundaries with adjustable absorption
Simulation Management
Execution via command line, Python API, or GUI
Surface impedance boundary condition (SIBC) model accelerated for broadband and harmonic simulations
Fully automated multi-port S-Parameter extraction (frequency domain and steady state)
Cloud-based execution with automated resource scaling
Legal
Intellectual property (IP) protection
Quasi-Static Electromagnetic Solvers
Sim4Life’s structured Quasi-Static Electromagnetic Solvers (P-EM-QS) and their unstructured counterparts (P-EM-UQS) enable efficient modeling of static and quasi-static EM regimes by applying the finite element method on graded voxel-based and unstructured meshes. The solvers support message passing interface (MPI) parallelization, and offer optimized performance in terms of accuracy and speed over a wide range of EM regimes. The P-EM-QS family of solvers is ideal for handling heterogeneous, complex geometries, while the P-EM-UQS family of solvers provides superior discretization of fine features in CAD-based geometries.
Application Areas
EM modeling and optimization in low-frequency and static regimes
Biomedical device design and optimization
Wireless power transfer (WPT) systems
Impedance, cross-talk, interference
In-to-in-body and on-to-in-body link optimization
Exposure assessment (support for LF exposure metrics from international standards on exposure safety)
Neuroprosthetics (retinal, cochlear, vestibular, motor prosthetics)
Bioelectronic medicine and ‘electroceuticals’ (modulation of peripheral nervous system to restore/control organ physiology and the immune system)
Brain-machine interfaces (BMI)
EEG/ECG
Neural sensing for closed-loop controlled, state-synchronized therapies and diagnostics
MRI gradient coil design and peripheral nerve stimulation (PNS) assessment combining the Gradient Coil Designer with coupled EM–electrophysiology simulations on neuro-functionalized anatomical models (see Neurostimulation use case)
Low-frequency hyperthermia (e.g., with nanoparticles)
Solver Capabilities
Electrostatic (ES) and electro quasi-static (EQS) regimes
Magnetostatic (MS) and magneto quasi-static (MQS) regimes
Multiport (e.g., multi-contact electrodes)
Integration with thermal solvers for induced heating
Direct coupling of multiport simulations to T-NEURO and thermal simulations: the admittance matrix is passed on with per-port weights, so electrode configurations can be swept or optimized without scripts
Automatic simulation termination upon convergence
Support for measured magnetic field data from MAGPy as a source to simulate induced currents in a phantom or ViP models
Support for localized refinement and conformal adaptivity
Run-time convergence monitoring
Efficient MPI parallelization for large problems
Sophisticated numerical solvers and preconditioners
Postprocessing time modulation (e.g., non-ideal electrode interfaces)
Four parameterized tissue/electrode contact impedance models, enabled with a single setting, that account for non-ideal interfaces in the pulse shapes applied to neuron or thermal simulations
Materials and Properties
Floating metals in ES and EQS
Anisotropic and inhomogeneous conductivity tensors
Support for medical imaging-based or diffusion simulation-based heterogeneous and anisotropic conductivity maps
Thin resistive layer insertion (virtual and real)
Extensive, continually maintained and reviewed tissue material properties database
Sources and Boundary Conditions
Current sources, cylindrical surface current sources
Vector potential sources
Dirichlet, flux boundary conditions
Simulation Management
Execution via command line, Python API, or GUI
Cloud-based execution with automated resource scaling
Legal
Intellectual property (IP) protection features
Thermodynamics Solvers
The Thermodynamics Solvers (P-THERMAL) enable the modeling of heat transfer in living tissues using advanced perfusion and thermoregulation models. The solvers include a finite-difference time-domain (FDTD) and a steady-state finite volume solver. Both structured, rectilinear voxel discretizations (ideal for heterogeneous systems and/or complex geometries) and unstructured meshes (good for highly accurate representations of fine CAD structures) are supported. Exclusive thermal damage and effect quantification models (e.g., T-CEM43 thermal dose) are included.
The ThermoReg whole-body thermoregulation simulator complements these solvers. It implements the UTCI-Fiala multi-node model, which forms the basis of the Universal Thermal Climate Index (UTCI) and has been validated against the COST Action 730 database. ThermoReg captures compartmental heat transport, arterial and venous counter-current exchange, respiration, and radiative and convective exchange. It also models sweating, shivering, vasoconstriction and vasodilation, and metabolic heat generation. Coupled with Sim4Life's electromagnetic and acoustic modeling of in vivo power deposition, ThermoReg addresses energy-intensive applications such as MRI scanning, thermal therapies (e.g., hyperthermic oncology), 5G base-station exposure, and heat stress across varying climates. It resolves regional temperature evolution, thermoregulatory responses to time-varying exposure, and energy transfer/budgets in both transient and steady-state analyses. A functionalized version of the Virtual Population's Duke model translates simulated power deposition into compartmental heat sources for the whole-body model.
Convective heat transfer (boundary conditions and active velocity fields)
Thermal ablation and tissue damage (necrosis, apoptosis, etc.) metrics
Thermal dose assessments for thermal therapies
Sources and Boundary Conditions
EM, acoustic, and customized heat sources
Coherent and incoherent sources
Flexible boundary conditions (Neumann, Dirichlet, and mixed for every interface and direction)
Pulsed and transient heat sources
Simulation Management
Execution via command line, Python API, or GUI
Cloud-based execution with automated resource scaling
Acoustic Solver
The full-wave Acoustics Solver (P-ACOUSTICS) handles linear and non-linear pressure waves (dispersive properties, frequency mixing). It is optimized for computing the propagation of pressure waves through complex environments such as the human body, or highly heterogeneous media like the skull. Furthermore, the Acoustics Solver supports CT-based bone property maps to enable personalized focused ultrasound targeting.
Application Areas
Acoustic exposures
High- and low-intensity focused ultrasound (HI/LIFUS)
Design and optimization of diagnostic and therapeutic ultrasound devices
Safety and efficacy evaluations of ultrasound devices
FUS-based reversible blood-brain barrier disruption for the targeted enhancement of active agent delivery
Ultrasound-mediated drug release
Acoustic neuromodulation
MRgFUS neurosurgery (tumor ablation, neuropathic pain treatment, movement disorders, etc.)
Solver Capabilities
Linear and non-linear 3D full-wave solvers based on the Westervelt-Lighthill equation
Density variation term for handling heterogeneous materials and high contrast interfaces
Simulation of large ultrasonic arrays comprising hundreds to thousands of piezoelectric elements
Audible acoustics and therapeutic ultrasound applications
Thermal solver coupling for acoustic energy deposition-related temperature increases
Multi-core and multi-GPU acceleration (fastest pressure wave solver on the market)
Materials and Properties
Database of acoustic tissue properties
CT image-based bone property distributions
Dispersive materials
Perfect reflectors
Sources and Boundary Conditions
User-defined signal sources (pulse, step, saw, custom, etc.)
Inhomogeneous, perfectly matched layer boundary conditions for domain truncation (permits restriction of the computational domain without excessive padding)
Simulation Management
Execution via command line, Python API, or GUI
Cloud-based execution with automated resource scaling
Plugin Solvers: Fluid Dynamics, Structural Mechanics, and More
Sim4Life's open plugin framework lets users integrate their own or third-party simulators and solvers, such as OpenFOAM, FEniCS, and SionnaRT, expanding the range of supported physics applications and enabling custom workflows, all within a unified interface. Plugin simulators take advantage of Sim4Life's modeling, preprocessing, cloud computing, and postprocessing functionality. Plugin solvers are currently available in Sim4Life.web. A third-party solver, such as the ray-tracing solver SionnaRT, is added and configured through the plugin framework and integrated into the Sim4Life workflow. The OpenFOAM plugin runs open-source fluid dynamics and small-strain structural mechanics solvers directly from the Plugin Manager.
Application Areas
Blood flow with and without cardiovascular implants (e.g., wall shear stress)
Targeted drug delivery and controlled release (e.g., ultrasound-triggered liposomal release)
Small-strain mechanical analysis of devices and tissues
Radio propagation and wireless coverage with ray tracing (e.g., SionnaRT)
Custom finite element models of additional physics (e.g., FEniCS)
Integration of custom or in-house solvers into Sim4Life workflows
Neuronal Tissue Models
The Neuronal Tissue Models (T-NEURO) enable dynamic modeling of EM-induced neuronal physiology, from activation to inhibition, using multi-compartmental representations or generic models. Sim4Life integrates Yale University’s NEURON simulation environment, which is ideal for studying field-neuron interactions, optimizing neurostimulation devices, and assessing exposure safety. T-NEURO goes beyond the prediction of individual axonal responses – its unique implementation of the extended reciprocity theorem permits the simulation and analysis of whole-brain biosignals such as magneto-/electroencephalography (MEG/EEG), accommodating realistic neuronal sources, inhomogeneous dielectric environments, and various recording device geometries.
With BraiNN, stimulation fields from the low-frequency solvers drive GPU-accelerated whole-brain network models, more than 90x faster than conventional CPU-based solutions, and produce simulated scalp EEG, all in one Sim4Life project. See Tissue Models for details.
Application Areas
EM neurostimulation (spinal cord stimulation (SCS), deep brain stimulation (DBS), etc.)
Transcranial brain stimulation (electrical: transcranial alternating/direct current stimulation (TACS/TDCS), temporal interference stimulation (TIS); magnetic: transcranial magnetic stimulation (TMS))
Neural sensing (compound action potentials (CAP), electroencephalogram (EEG) and electrocorticogram (ECoG) recordings, local field potentials (LFP), etc.)
Catheter-based stimulation and sensing
Neuroprosthetics (retinal, cochlear, vestibular, motor prosthetics)
Bioelectronic medicine (‘electroceuticals’)
Magnetic resonance-guided focused ultrasound (MRgFUS) neurosurgery (tumor ablation, neuropathic pain treatment, movement disorders, etc.)
Low frequency EM exposure safety assessment (wireless power transfer, MR gradient coils, etc.)
Temperature-dependent neuronal dynamics
Neuro-motoric incapacitation
Neural interface design and optimization
Brain-machine interfaces (BMI)
BraiNN whole-brain network simulation coupled to low-frequency stimulation fields, with simulated scalp EEG
Dynamic modeling of EM-induced neuronal activation, inhibition, and synchronization
Simulation of axons, complex neuron morphologies, and neural networks
Coupling with EM-QS solver family
Accurate and efficient modeling of neural sensing
Hessian calculator for volume of tissue activated (VTA) estimation
Predefined models of myelinated and unmyelinated nerve fibers (incl. A-delta spinal afferents, SENN model for LF-exposure safety, etc.)
Support for the Aberra collection of 25 biophysically realistic human pyramidal neuron models from different cortical layers, available from the IT'IS Foundation
Simple import tools for complex, user-defined (Python and hoc-based) neuron models
.hoc file importer (including compressed .hoc files) for morphology- and electrophysiology-realistic neuron models from sources such as ModelDB, the Human Brain Project, and the Allen Brain Institute
Threshold and recruitment curve calculation via automated titration routines, with automated recruitment-curve analysis to compare stimulation strategies across fiber populations
Generalized Activating Function (GAF) predictor for rapid estimation of neural recruitment, including MRG-type double-cable axon models of sensory and motor fibers, with activation thresholds predicted up to 1000x faster than conventional neuron simulations
Stimulation selectivity indices for multi-fascicular nerves or spinal-roots
Detection of neuronal spikes (times and locations)
Predefined nerve trajectories and axon properties in NEUROCOUPLE ViP models
Capture and plotting of time-dependent membrane dynamics
Multiport low-frequency sources with per-port weights for assessing and optimizing stimulation by multi-contact electrodes
Synapse models
Diffusion tensor imaging (DTI)-based assignment of heterogeneous and anisotropic brain conducitivites (reflecting fiber orientations)
Thermal Tissue Damage Model
Sim4Life’s thermal dose and tissue damage models permit assessment of the impacts of heating on the physiology of living tissues. The CEM43 tissue heating model is a widely applied technique for quantifying the effects of transient heating based on direct, cytotoxic effects. CEM43 converts time-varying thermal exposures into an equivalent exposure time (in minutes) at 43°C. The approach permits quantitative comparisons of different transient heat exposure scenarios (e.g., short heating at high temperature vs. prolonged, moderate heating) by relating them to an established damage threshold. Sim4Life also provides direct assessments of tissue damage through the Arrhenius tissue damage model.
Application Areas
Hyperthermic oncology (efficacy evaluation and optimization)
Radiofrequency, microwave, and focused ultrasound ablation
MR safety with and without implanted devices
Electromagnetic and thermal exposure safety
Skin burn quantification
CEM43 and Arrhenius tissue damage models
Accurate (slow) or approximate (fast) model evaluation
Determination of effective iso-surfaces
Calculation of cumulative histograms (commonly used for treatment planning)
Database of Tissue Properties
The material parameter database, originated and maintained by the IT’IS Foundation, provides the computational life sciences community with recommended values for and associated variances of a growing list of biological tissue properties. The database is free, accessible online, and continually updated to reflect the highest quality measurements available. Sim4Life makes it easy for users to automatically assign tissue parameter values to the Virtual Population (ViP) models from the material properties database. In addition, Sim4Life supports image-based assignment of heterogeneous tissue property maps (anisotropic brain tissue conductivities, perfusion, acoustic propagation in bones) for enhanced realism and accuracy.
List of properties:
Frequency-dependent dielectric properties
Densities
Heat capacities
Thermal conductivities
Heat transfer rates
Heat generation rates
Viscosities
Acoustic properties (speed of sound, attenuation constant, and nonlinearity)
Tissue weight fractions
MRI parameters (longitudinal and transversal relaxation times)
Up-to-date and comprehensive estimates of tissue material parameter values
Strict quality assurance measures ensure accuracy and traceability
Statistical information (average, range, standard deviation) for each tissue
Axon morphometry for peripheral nerve trajectories as segmented in the ViP v4.0 models
Tissue elemental compositions (weight fractions) for standard tissues (e.g., as segmented in the ViP v3.x)
Image-based assignment of heterogeneous tissue property maps (applies to selected properties in LF, thermal, and acoustic solvers)
Perfusion Models
Perfusion is frequently the dominant factor influencing (and limiting) in vivo heating. Therefore, our thermal solvers offer advanced models of perfusion and vasculature, including active thermoregulatory processes known to affect perfusion rates.
Pennes’ bio-heat equation-based perfusion with extensions (e.g., tensorial effective tissue conductivity accounting for anisotropic blood flow)
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