DesignSafe AI Training#
Hands-on AI training — running modern AI workflows on
DesignSafe with the DesignSafe API
(dapi).
Clone the whole repo into DesignSafe (one click)#
Clones — or fast-forwards — this repo into MyData/ds-training/ai/ and opens this README.
Re-click any time to pull the latest version.
Scientific Machine Learning for Engineers#
# |
Module |
The network learns |
Maps |
Cost of a new query |
Notebook |
|---|---|---|---|---|---|
1 |
3 coefficients |
\(\mathbb{R}^3 \to \mathbb{R}\) |
instant |
|
|
2 |
a nonlinear surrogate |
\(\mathbb{R}^3 \to \mathbb{R}\) |
instant |
|
|
3 |
why the model said that |
— |
instant |
|
|
4 |
one solution field |
\(x \mapsto w(x)\) |
retrain |
|
|
5 |
the solution operator |
\(q(\cdot) \mapsto w(\cdot)\) |
instant |
|
|
6 |
someone else’s foundation model |
image \(\mapsto\) debris map |
instant |
|
Modules 1–3 are data-driven: fit a flexible model to simulation output, then
interrogate it. Modules 4–5 are physics-driven: build the governing equation
into the model so it cannot learn something the physics forbids. Module 6 leaves
the beam behind for real imagery and a pre-trained foundation model — the applied
counterpart to everything before it.
Notebooks are published to
CommunityData/Training/2026-SPARC/Day3/3b-SciML/.
Requirements#
Modules |
Needs |
|---|---|
2–5 |
CPU only. A few minutes each on DesignSafe JupyterHub or Colab. No allocation, no GPU. |
1 |
A TACC allocation (submits to Stampede3 via |
6 |
Parts 1–2: a DesignSafe session, GPU recommended. Parts 3–4: a TACC allocation. |
The modules#
Module 1 — Regression on HPC#
01-opensees-ml/ · details
Sweeps a 2D cantilever pushover in OpenSeesPy across NodalMass x LCol x E on
Stampede3 via dapi and PyLauncher, then fits a linear regression that recovers
log T = const + 0.5·log M + 1.5·log L - 0.5·log E <=> T = 2π·√(M·L³/3EI)
The coefficients come back as [0.500, 1.500, -0.500] with R² = 1.0. They are
not abstract weights — they are the physical exponents, so a correct pipeline
must recover them. That makes the regression a test of the entire workflow.
The catch that sets up Module 2: it worked because we knew to take logarithms.
The folder also holds an optional variant, 01-opensees-ml-workflow-dag.ipynb,
which runs the same study as an explicit two-job DAG (sweep → train) through
DesignSafe’s workflow service, so a retrain costs one core instead of a 48-core
resweep. Not part of the three-hour agenda; published as a sub-section.
Module 2 — Multi-Layer Perceptrons#
02-mlp/
Hands the same 75 runs to a linear model on raw features. It scores
R² = 0.984 — and predicts negative fundamental periods, with 121% error on the
stiffest columns. An MLP on the same raw features recovers the physics without
being told about logarithms.
Covers: why stacked linear layers collapse (W₂W₁ = W̃), the Universal
Approximation Theorem and its four caveats, the role of width, reverse-mode AD (used
here for design sensitivities ∂T/∂L, and the mechanism behind Module 4), and
overfitting. Ends by fitting the beam’s deflection field from 8 sparse readings
— which fails at the support, motivating physics in the loss.
Two findings worth flagging, both measured live in the notebook and both contradicting the usual story:
Changing the random seed moves the test error as much as an 8× change in width. Report seed spread before claiming an architecture is better.
A 198,657-parameter network on 60 points interpolates the training set (loss
4e-11) while its test error stays level with a 1,217-parameter one’s. The train/test ratio screams7.6e+07; the predictions are fine. Judge on test error, not the gap — and note that 2% label noise costs far more accuracy than any architecture change did.
Module 3 — Explainable AI#
03-xai/
Module 2 traded three interpretable exponents for thousands of opaque weights. This module buys the interpretation back, on 7,291 real field observations of lateral spreading (four predictors: groundwater depth, distance to free face, slope, PGA).
Decision tree (readable) → XGBoost (accurate, opaque) → three definitions of “feature importance” compared → SHAP for attributing a single site’s prediction, with the efficiency axiom verified numerically → beeswarm and dependence plots for global structure.
The importance comparison lands somewhere more useful than “they disagree”: all three agree on the ranking, but slope scores ~15% by gain and ~3% by permutation. That gap is a diagnosis, not a contradiction — the model uses slope but does not need it, because the other site-geometry features carry the same information. It is also why permutation importance is unreliable under correlated features.
The real payoff is stated plainly: XAI’s value is not explaining a model that is right, it is catching one that is wrong for right-looking reasons — a SHAP direction that contradicts the physics is a bug you would otherwise have shipped.
Module 4 — Physics-Informed Neural Networks#
04-pinn/
The viewpoint shifts: the network’s input becomes a coordinate, its output the
field, and training is solving the PDE. Solves EI·w'' = -P(L-x) for the
cantilever with zero labelled data.
Covers non-dimensionalisation (without it the residual sits 10⁵ above the
solution — the most common reason a hand-rolled PINN will not train), the residual
via create_graph=True, soft vs hard boundary conditions (W = ξ²·N(ξ)
satisfies the clamped end exactly), and the inverse problem.
The inverse problem is the headline: recover EI from 8 noisy deflection
readings as one extra nn.Parameter. It lands within 0.5%.
Part 5 is deliberately unflattering and important:
As a forward solver a PINN is ~10⁵× slower than the exact solution, and less accurate. Do not sell it as a fast solver.
A PINN is accurate only in the quantities you put in the loss — the constrained
w''is excellent, the unconstrainedw''''is garbage.No λ for the boundary penalty was best at everything; turning λ up — the usual advice — degraded the solution by three orders of magnitude.
Module 5 — Operator Learning#
05-operator-learning/ · cantilever example after Somdatta Goswami
Module 4’s PINN solved one load case. DeepONet learns the whole solution
operator q(·) ↦ w(·): 1000 GRF-loaded finite element solves of a 2 m × 0.2 m
cantilever, 100 input sensors, 1314-node output field.
Covers the Chen & Chen (1995) operator UAT read literally as an architecture
(branch × trunk, combined by one einsum) and a naive-MLP baseline whose cost is
measured, not asserted — more than 10× the cost per step at full resolution, because it
re-encodes the input function at every query point.
The payoff is the learned trunk basis, examined properly. The trunk’s leading four directions land within ~12° of the four leading POD modes of the FE data — it rediscovered the dominant deformation modes unaided. But the basis is non-orthogonal (mean |cosine| ~0.4), ill-conditioned (~10⁵), and unordered past those few, so unlike POD it cannot be truncated: 50 of 100 modes still leaves 35% error where POD is under 1% by 10. A DeepONet is a learned spectral method with an unmanaged basis — right subspace, wasteful representation — and the module covers the three standard fixes, including POD-DeepONet.
Ends with the limits (800 FE solves are the real cost; out-of-distribution input functions fail silently; the input grid is fixed — which is what FNO fixes) and with physics-informed DeepONet, which combines Module 4’s residual with Module 5’s operator structure.
Ported to PyTorch so the whole session uses one framework; the original JAX/Flax version is at DesignSafe-Training/deeponet.
Module 6 — Computer Vision at Scale#
06-clipseg/ · details
· Kooshan Amini and Jamie E. Padgett, Rice University
Modules 1–5 built models of a cantilever beam — small, tabular, always checkable against a closed form. This module is the other kind of work.
CLIPSeg-debris is a text-prompted, 3-class segmentation model that maps hurricane debris (no / low-density / high-density) in post-event aerial imagery, built on the CLIP foundation model. The notebook runs it end to end:
Official inference on the published debris dataset (PRJ-6029).
A regional debris map for Hurricane Ian (2022) on Estero Island, FL, from live NOAA Emergency Response Imagery.
Scale-out inference as a GPU HPC job via
dapi.Fine-tuning on TACC Vista (GH200), tracked on Weights & Biases or DAPI.
What changes from the earlier modules: 75–1000 simulated cases become published imagery and live rasters; a hand-written network with thousands of parameters becomes a pre-trained model with ~150 M; minutes of CPU training becomes hours on a GH200; and a closed-form check becomes hand-labelled masks.
The model is deliberately not re-implemented — the notebook loads and runs the official published repository (PRJ-6225) as distributed. That is the lesson: most applied ML is not writing architectures, it is getting someone else’s model to run correctly on your data, at scale, reproducibly.
The single best exercise here is the first one in Your turn: CLIPSeg is text-prompted, so the class names are an input, not a fixed head. Change “debris” to “rubble” and watch the map move — that is what separates a foundation model from a fixed classifier.
Parts 3–4 need a TACC allocation; Parts 1–2 run without one (GPU session recommended). Weights & Biases is optional — training can be tracked through DAPI.
Going deeper#
These modules are distilled from a full semester course. The long-form treatments — with the proofs, the derivations, and the interactive demos — live in the SciML course, which also covers Fourier Neural Operators, graph network simulators, SINDy, neural ODEs, function encoders, and Bayesian SciML.