“We needed a partner who understood both the technical depth of Azure OpenAI and the governance requirements of an enterprise.”
Enterprise Program Brief
Introduction to Physics-Informed Machine Learning With NVIDIA Modulus
Introduces Modulus and the basics of physics-informed deep learning for faster simulation-driven workflows.
NVIDIAAdvancedEnterprise Track
Download Program Brief (PDF)Program Snapshot
Engagement scoped to cohort
Duration2 hours
FormatVirtual, On-site, or Hybrid
LevelAdvanced
SubjectDeep Learning
- MCT-led delivery
- Private cohort / virtual ILT
- Typical response: 1 business day
Scoping call typically scheduled within one business day
VNode ITeS delivers Introduction to Physics-Informed Machine Learning With NVIDIA Modulus as an MCT-led NVIDIA program for Scientific ML Practitioner. Typical duration is 2 hours (Virtual, On-site, or Hybrid). Labs follow production-shaped scenarios rather than slide-only walkthroughs. Private cohorts can shift emphasis by role mix, workspace or repo constraints, and rollout timing.
Audience Profile
Built for these roles
Built for teams bringing machine learning into engineering and scientific simulation workflows.
- Scientific ML Practitioner
- Deep Learning
- Tailored Team Delivery
- Implementation-Focused
Overview
Executive overview
Official NVIDIA self-paced course introducing physics-informed machine learning with NVIDIA Modulus.
Prerequisites
- Python familiarity and an understanding of partial differential equations or equivalent engineering context.
Program Outcomes
Capabilities your teams will gain
- Understand physics-informed ML concepts
- Recognize how Modulus supports simulation use cases
Enterprise Delivery
Official learning & delivery options
Review the vendor learning resource, then book a consultation or request a private cohort for your team.
Curriculum
Curriculum roadmap
- Physics-informed ML foundations
- Modulus workflow basics
1Module 1
Use ML for simulation-heavy problems
+
Module 1
Use ML for simulation-heavy problems
Learn the basic ideas behind physics-informed deep learning and NVIDIA Modulus.
- Physics-informed ML foundations
- Modulus workflow basics
Delivery Models
Delivery models
Virtual ILTOnsiteHybridExecutive WorkshopBootcampWeekend
Engagement Fit
Engagement fit
- Implementation-focused labs
- Private cohort delivery
- Advanced practitioner depth
- Business outcome alignment
Enterprise Customization
Enterprise customization
Tailor this program to your organization's priorities: Helps engineering teams accelerate simulation-led workflows with physics-informed deep learning.
- Use your simulation domain scenario
Credentials
Certification & official source
Aligned to the official source referenced for this program.
View Official SourceResources
Program resources
Yes. Most enterprise clients prefer private delivery scoped to role mix, timezone, and rollout timeline. We align lab environments and scenarios to your tenant context where applicable.
Enterprise Proof
Trusted delivery outcomes
Retail & E-commerce
Representative Retail Analytics Team
Instead of treating reporting as a tooling issue alone, the work focused on consistency, governance, and shared delivery practices across analysts and engineering teams.
- Higher consistency in report design practices
- Improved collaboration between analysts and engineering teams
Healthcare
Representative Healthcare Product Team
The engagement helped product and engineering stakeholders move from interest in AI to clearer implementation choices, security expectations, and prototyping discipline.
- Stronger alignment between product and engineering teams
- Improved clarity on prototype-to-production requirements
Delivery Capability
Enterprise-grade instruction
MCT-led delivery
Programs led by Microsoft Certified Trainer practitioners
Enterprise program oversight
Founder-led specialist delivery with structured rollout planning
Global delivery
APAC · EMEA · Americas · Virtual & Onsite
Implementation-focused
Hands-on labs aligned to production scenarios
