deepmath is a deep-tech company specializing in ad vanced mathematical modeling, engineering simulations, and AI-enhanced engineering. We work where standard tools or workflows reach their limits, helping industry solve complex physical and operational challenges using methods such as Finite Element Methods (FEM), Computational Fluid Dynamics (CFD), Discrete Event Simulations (DES), and different forms of Artificial Intelligence (AI).
Startups, SMEs, and R&D teams facing high technical uncertainty in energy, off shore, marine, and advanced engineering systems.
When simulations are too slow, too expensive, unreliable, or hard to automate. Or when you need solid feasibility answers early in your project.
deepmath delivers domain‑specific solutions across Renewable Energy (offshore wind, floating solar), Offshore Engineering (naval and offshore systems), and Marine Engineering & Environment (coastal and ocean applications). In addition, we provide tailored solutions for the specific challenges inherent to Innovation & R&D, including technical fea sibility studies, hybrid physics–AI workflows, rapid prototyping, digital twins, automation, and novel method development.
In energy, we support renewable energy sys tems including offshore wind and floating solar, delivering numerical laboratories and prototypes, fea sibility studies, performance prediction, and design optimization. In offshore & naval engineering, we address hull hydrodynamics, seakeeping, station keeping and control, mooring and riser systems, structural integrity, and fatigue life management. In marine engineering & environment, we model coastal and ocean dynamics, wave–current interactions, sediment transport, and environmental impact assessments for infrastructure and operations. Broadly in innovation & R&D, we provide tailored solutions for innovative startups to assess and harness the full power of engineering simulations and AI, through scoping and feasibility studies, risk reduction,hybrid physics–AI workflows, rapid prototyping, digital twins, and automation that accelerate verification and engineering decision-making.
Feasibility studies, high-fidelity simulations, multiphysics coupling, digital twins, AI-accelerated models, and workflow automation. Including for example hydro dynamic modeling (CFD and potential-flow), fluid–structure interaction and aero-hydro-servo-elastic coupling, mooring and station-keeping analysis, fatigue and reliability assessments, environmental load modeling (waves, wind, current), uncertainty quantification and sensitivity analysis, design space ex ploration and optimization, surrogate models and AI/ML accelerators, predictive and generative AI, physics–informed learning, digital twins for real-time inference and operational forecasting, data assim ilation and calibration, and automation of pre- and post-processing pipelines.
No. We build on top of existing tools, improving accuracy, automation, and decision speed.
Yes. We design and develop custom in-house tools, models, and workflows that integrate directly into your existing engineering stack.
Yes. We offer simulation and modeling expertise to assess hydrodynamics, structural interactions, and environmental effects in off shore wind farms and floating solar installations.
Absolutely. We collaborate with SMEs and innovative startups seeking disruptive mathematical and simulation solutions.
Yes. We frequently support startups with evolving designs, tight timelines, and strong uncertainty.
Deliverables typically include validated models and configuration f iles; simulation datasets, plots, and decision‑ready engineering reports; design recommendations with KPIs and trade‑offs; and, when in scope, reusable tools such as parameterized templates, automation scripts/pipelines, notebooks/dashboards, and source code for custom models or surrogates. We provide documentation and handover sessions, version‑controlled assets, and, if required, deployment artifacts for on‑prem or cloud. IP ownership and licensing are defined contractually; formats and standards are aligned with your toolchain and compliance requirements.
We privilege our local infrastructure when pos sible, running on clusters up to 128 CPUs with 254GB RAM (growing steadily) with latest NVIDIA GPUs. We also employ on-demand cloud compute on Azure and AWS, supported through their startup programs, when scale, burst capacity, or proximity to data/services is required.
Yes. All projects run under NDA, with strict data governance. We can also work on client infrastructure.
Data is provided by our clients under NDA and full control, via secure channels and least‑privilege access. We can work with anonymized or aggregated extracts and, when preferred, run on your infrastructure. We complement client data with authoritative public/licensed sources and generate synthetic data from validated simulations. All data flows follow strict governance policies and are compliant with GDPR/CCPA.
We operate across Europe, the United States, and Brazil, delivering remotely or on‑site as needed, in English, French, and Portuguese, with compliance to local data and safety requirements.
We collaborate with leading industrial and academic partners, providing in dustry‑leading expertise and enabling cross‑domain knowledge transfer. We also participate in startup programs including NVIDIA Inception, AWS Activate, Microsoft for Startups, and the Ansys Startup Program.
Contact our team with your challenge, goals, and timeline. Email liadpaskin@deepmath.tech or brunotessaro@deepmath.tech ; we will follow up with next steps.
We simulate floating PV systems by com bining hydrodynamic modeling (CFD or potential-flow) with structural and mooring dynamics to predict platform motions, connector loads, mooring tension, and performance under realistic waves, wind, and current.
Enabling optimization and risk reduction, we use multi-physics coupling (hydrodynamics + structural response + mooring interac tions) to capture relative motions between connected platforms, connector loads, and array-size/layout effects.
We run dynamic mooring analysis (e.g., Or caFlex or custom workflows) for catenary/taut systems under metocean loading to evaluate station keeping, peak tension, fatigue life, reliability, and sensitivity to environmental uncertainty.
Yes. We build digital twins that fuse vali dated physics models with data assimilation and AI-enhanced inference for real-time monitoring, pre dictive maintenance, operational forecasting, and decision support.
Weperformaero–hydro–servo–elastic simulations coupling structural dynamics, hydrodynamics, aerodynamics, and control to predict turbine motions, platform response, mooring loads, and fatigue performance across operational and extreme conditions.
Wecouplehydrodynamics(potential f low/CFD) with mooring dynamics (e.g., OpenFAST or custom workflows) to predict station keeping, drift, and load transfer to anchors/structure, including fatigue/reliability and uncertainty quantification for environmental variability.
Yes. We cluster load cases and employ physics-aware surro gates to speed design exploration, screening, and optimization while preserving verification and validation standards.
Yes. Through data assimilation and model cal ibration we correct biases in low-fidelity and reduced-order models using field measurements, yielding multi-fidelity predictions that are faster and better aligned with observed behavior.
Depending on fidelity needs, we apply potential-flow and/or CFD to compute motions, accelerations, added resistance, slamming met rics, and wave loads, and derive seakeeping/operational envelopes for design refinement and decisions.
Yes. We analyze open-water performance, propeller–hull inter action and wake alignment, cavitation and pressure pulses, and thrusters/azimuthing units. Depending on scope, we use RANS CFD with rotating frames/sliding meshes, actuator disk/line or blade-element methods, and couple to structural vibration and noise models when required.
Yes. We model mooring and riser systems with dynamic analysis and couple with hydrodynamic/structural solvers as needed for station keeping, fatigue damage, extreme-event response, and reliability.
We run FEM-based analysis for stress/deforma tion, nonlinearity, buckling/eigenmodes, and transient response, then integrate fatigue life management and reliability to quantify sensitivity and safety margins.
Yes. WedevelopautomatedCFD FEM FSI workflows for strong load/response coupling (e.g., wave loading on structures, dynamic re sponses, mooring/hull interactions), validated via convergence and benchmark studies.
We build load models from author itative public/licensed datasets and simulation‑driven workflows to compute operational and extreme conditions, with uncertainty quantification for confidence bounds and sensitivities.
We primarily use statisti cal hindcasts/reanalysis and reduced‑order hydrodynamics for planning and design envelopes, applying CFD only when local flow/interaction must be resolved (e.g., complex bathymetry or near‑structure effects).
We quantify wind profiles, stability (stratification), turbulence intensity, shear, and extreme winds using reanalysis and mesoscale models, LiDAR/nacelle data, and statistical hindcasts. High‑fidelity CFD (RANS/LES) is applied when local terrain/array effects demand detailed resolution.
We couple spectral wave models with hydrodynamic cir culation to capture refraction, Doppler shifting, blocking, and shear effects; when relevant, we include nonlinearity (triads/quartets) for nearshore or energetic seas. Outputs provide joint sea states and kinematics for design and operations.
Sea state alters MABL stability and turbulence, changing inflow conditions and wake behavior. Besides inducing fluctuations in the sourcing wind speed, waves also induce platform motion that feeds back on power capture, loads, and fatigue.
Yes. We predict erosion, deposition, and seabed evolution under currents and waves for cable routing, foundation stability, dredging, ports, and environmental impact assessment.
Yes. We quantify impacts such as flow alteration, sediment plumes, and coastal response; support scenario comparisons and calibration to observations; and produce reporting suitable for permitting better-informed decision-making.
Our core methods include Finite Ele ment Methods (FEM) for physical structures, Computational Fluid Dynamics (CFD) for fluid behavior, Discrete Event Simulations (DES) for system dynamics, and Artificial Intelligence (AI) for accelerating predictions and automation. We also couple these methods for multiphysics and system-level workflows.
FEM solves partial differential equations in a weak (varia tional) form by discretizing the domain into elements and approximating fields with basis functions. It excels at structural and multi-physics problems with geometric complexity, material and geomet ric nonlinearity, contact, buckling/eigenvalue analysis, and transient response; mesh refinement and convergence studies ensure verified results.
CFD numerically solves the Navier–Stokes equations to predict fluid flow, pressure, and transport. In industry, solvers are predominantly finite volume (FVM) on structured/unstructured meshes; we also apply finite element formulations where advantageous. We select turbulence closures (RANS/URANS/LES), model free-surface and multiphase effects as needed, and couple with structures for fluid–structure interaction when required.
Potential-flow/BEM solve inviscid, ir rotational flow and linear/free-surface wave problems efficiently in frequency or time domain. They are well-suited to wave loads, seakeeping, diffraction/radiation, RAOs, and multi-body hydrodynamics (in cluding connectors), providing fast, verified predictions when viscous separation is limited; we use CFD locally when viscous effects dominate.
DES models systems whose state changes at discrete event times (e.g., arrivals, departures, failures), enabling rigorous throughput, queueing, scheduling, and logistics analyses. It is effective for operations planning, maintenance strategies, port and offshore logistics, and resource allocation under uncertainty, and integrates naturally with stochastic models and Monte Carlo experimentation.
We apply predictive and generative ML tailored to engineering data: foundation models adapted to spatial fields, meshes, and sequences (CNNs for fields, GNNs for meshes/graphs, transformers for temporal and spatio–temporal signals), physics–informed learning and operator learning (e.g., PINNs, FNO/DeepONet), surrogate and reduced–order models, active learning and Bayesian optimization for design exploration, uncertainty quantification (ensembles and Bayesian inference), and data assimilation for robust digital twins.
FEM–CFD fluid–structure interaction for offshore platforms and moorings; aero–hydro–servo–elastic simulations for floating wind turbines (structure, hydrodynam ics, aerodynamics, and control); data assimilation with CFD/BEM to build operational digital twins and forecasting tools; DES integrated with physics-based models for port and offshore logistics under metocean uncertainty; predictive AI surrogates with uncertainty quantification for rapid design space exploration; and end-to-end workflow optimization with generative AI.
AI will not replace but enhance physics simulation by optimizing the entire end-to-end workflow. We use physics-aware ML to automate pre/post-processing, generate designs and load cases, accelerate solvers via surrogates and operator learners, guide design loops with active learning and Bayesian optimization, quantify uncertainty, and sustain digital twins with data assimilation. Examples include AI screening of FEM–CFD FSI configurations, generative scenario synthesis for floating-wind aero–hydro–servo–elastic studies, and ML-augmented DES logistics under metocean uncertainty.
Weusestate-of-the-art commercial and open source tools alongside in-house developments. We deliver on ANSYS and ORCINA stacks, or fully open source workflows (e.g., OpenFOAM for CFD, Code_Aster for FEM, OpenFAST for wind turbines...), and hybridize with custom Python/C++ and GPU-accelerated components to meet performance, inte gration, security, and licensing constraints.
We employ ANSYS, e.g., ICEMCFD for meshing, (py)Fluent for CFD, (pyAPDL) Mechanical for structures; for high‑fidelity flow, structural, and hy drodynamic analyses. And ORCINA OrcaWave and OrcaFlex for BEM, moorings, risers, cables, and multy-physics dynamic systems. We co‑simulate and exchange loads/kinematics with FEM/CFD, au tomate studies via Python, and augment with AI surrogates for rapid trade‑offs under rigorous V&V.
Open-source tools provide transparency, auditability, and reproducibility for regulated and safety‑critical engineering, while reducing vendor lock‑in and licensing constraints. They enable rapid prototyping, community‑validated methods, and seamless integration with custom code, HPC, and cloud. When required, we harden and validate open-source stacks to your QA, security, and compliance standards.
In-house development fills capability gaps and delivers performance, reliability, and integration beyond off‑the‑shelf tools. We build specialized solvers, couplers, and automation to meet stringent fidelity, runtime, and interoperability goals, all while ensuring IP control and long‑term main tainability.
deepmesh is an intelligent mesh generation technology that uses AI to automate and improve creation of solver-ready meshes for CFD and FEM, addressing a major bottleneck in engineering workflows.
Traditional meshing is time-consuming and fragile, especially for com plex CAD or large parametric studies. AI adapts to geometry and quality criteria, reducing manual trial-and-error and producing consistent meshes at scale.
deepmesh combines technologies well-suited to unstruc tured meshes and complex geometric data: graph neural networks, transformer models, and self-play reinforcement learning.
Meshes are naturally graphs (nodes/edges encode geom etry and connectivity). GNNs capture local structure and neighborhoods; transformers capture global context via attention for long-range geometric interactions.
Training starts with supervised learning on meshes produced by estab lished commercial meshing software to learn industry practices and quality criteria used in production workflows.
Supervised models are limited by the data they imitate and cannot surpass the capabilities or assumptions embedded in the training meshes alone.
Self-play treats meshing as a game, using quality cri teria as feedback to iteratively discover strategies that outperform the initial supervised baseline and generalize across geometries and physics.