Time-Dependent Vector Fields and the Need for Generalization
The
fundamental theorem on flows
of the previous development settled the existence and uniqueness theory for systems
of ordinary differential equations on a smooth manifold in the autonomous case —
that is, when the law specifying the velocity at each point depends only on the
point and not on the moment of time at which the trajectory passes through it. The
autonomous restriction is natural for the geometric content of the theory (a
vector field
on \(M\) assigns a single vector to each point), and it covers most of the
constructions encountered so far. There are, however, several settings in which an
explicit time-dependence in the velocity law cannot be removed by reformulation:
classical mechanics with externally driven forces, control systems with prescribed
time-varying inputs, and — more recently — flow-based generative models in machine
learning, in which a neural network produces a time-dependent velocity field whose
flow transports a base probability distribution into a target one. The present
section establishes the existence and uniqueness theory in this non-autonomous
regime, and identifies the time-dependent flow that takes the role played by the
autonomous flow in the previous theorem.
Time-Dependent Vector Fields
Definition: Time-Dependent Vector Field
Let \(M\) be a smooth manifold and \(J \subseteq \mathbb{R}\) an open interval.
A time-dependent vector field on \(M\) with parameter interval
\(J\) is a smooth map
\[
V : J \times M \to TM
\]
such that \(V(t, p) \in T_p M\) for every \((t, p) \in J \times M\). For each
fixed \(t \in J\), the assignment \(V_t : p \mapsto V(t, p)\) is then an
ordinary smooth vector field on \(M\).
The map \(V_t\) is a vector field on \(M\) — a slice through the time-dependent
field at time \(t\) — and the family \(\{ V_t : t \in J \}\) is the data that
replaces a single vector field when the velocity law is allowed to vary with time.
Every ordinary smooth vector field \(X \in \mathfrak{X}(M)\) determines a
time-dependent vector field on \(\mathbb{R} \times M\) by the constant assignment
\(V(t, p) = X_p\); the autonomous theory is therefore contained in the
non-autonomous theory as the special case in which \(V_t\) does not depend on \(t\).
Integral Curves of a Time-Dependent Vector Field
The differential equation that an integral curve of a time-dependent vector field
must satisfy is the natural one. At each parameter value \(t\), the curve must have
velocity equal to the value of the time-dependent vector field at the pair
\((t, \gamma(t))\) — that is, evaluated both at the current time and at the current
point.
Definition: Integral Curve of a Time-Dependent Vector Field
Let \(V : J \times M \to TM\) be a time-dependent vector field on \(M\). A
differentiable curve \(\gamma : J_0 \to M\), defined on an open subinterval
\(J_0 \subseteq J\), is an integral curve of \(V\) if
\[
\gamma'(t) = V\bigl(t, \, \gamma(t)\bigr)
\qquad \text{for every } t \in J_0 .
\]
When \(V\) is the time-dependent extension of an autonomous vector field
\(X\) — that is, when \(V(t, p) = X_p\) — the equation
\(\gamma'(t) = V(t, \gamma(t)) = X_{\gamma(t)}\) reduces to the definition of an
integral curve
of \(X\). The non-autonomous definition is therefore a genuine generalization of the
autonomous one, recovering the latter as a special case.
Why a Time-Dependent Vector Field Need Not Generate a Flow
The autonomous fundamental theorem produced not merely an integral curve through
each point, but a smooth flow whose trajectories were the integral curves and whose
composition was governed by the group law of \(\mathbb{R}\). The flow assembled the
family of integral curves into a single map \(\theta : \mathcal{D} \to M\) with the
property that for each point of \(M\), only one trajectory passes through it. In the
time-dependent case this consolidation fails at a basic level: two integral curves
of \(V\) that pass through the same point \(p\) at different starting times can
leave \(p\) in different directions, because the velocity at \(p\) is a function of
the time at which the trajectory arrives there.
Concretely, suppose \(\gamma_1\) and \(\gamma_2\) are two integral curves of
\(V\) — each with its own parameter interval — that both pass through the same
point \(p\) but at different parameter values: \(\gamma_1(t_0) = \gamma_2(s_0) = p\)
for some \(t_0 \neq s_0\). The integral-curve equation forces their velocities at
\(p\) to be the values of \(V\) at the corresponding parameter values, namely
\(\gamma_1'(t_0) = V(t_0, p)\) and \(\gamma_2'(s_0) = V(s_0, p)\); these two
velocities need not agree, so the two curves depart from \(p\) along different
paths. In the autonomous case this cannot happen, because the velocity at \(p\)
is a single vector \(X_p\) independent of any time parameter; in the
time-dependent case the same point \(p\) carries a whole one-parameter family of
possible departure velocities.
The object that replaces the autonomous flow in this setting is therefore not a
map parametrized by a single time variable \(t\), but a map parametrized by a pair
of time variables \((t, t_0)\) — the current time and the starting time — together
with the starting point. The next two sections produce this object as a
well-defined smooth map and establish its basic properties; the construction
proceeds by a single technical reduction that transports the entire non-autonomous
theory back to the autonomous theorem already proved.
The Lifting Trick
The reduction from time-dependent to autonomous is geometric in nature: we enlarge
the manifold by one dimension to include the time variable as an additional
coordinate, and construct on the enlarged manifold an honest autonomous vector
field whose flow encodes both the passage of time and the motion produced by the
time-dependent field on the original manifold. This is the lifting trick: the
non-autonomous problem on \(M\) is lifted to an autonomous problem on
\(J \times M\), and the answer on \(J \times M\) is then projected back down to a
time-dependent flow on \(M\).
Construction of the Lifted Vector Field
Let \(V : J \times M \to TM\) be a time-dependent vector field, and let
\(s\) denote the standard coordinate on the open interval \(J \subseteq \mathbb{R}\).
The
product manifold tangent space identification
decomposes the tangent space at each point of \(J \times M\) as a direct sum,
\[
T_{(s, p)} (J \times M) \cong T_s J \oplus T_p M ,
\]
so a tangent vector at \((s, p)\) is specified by giving a tangent vector to \(J\)
at \(s\) and a tangent vector to \(M\) at \(p\). Define
\(\tilde V \in \mathfrak{X}(J \times M)\) by assigning to each point \((s, p)\) the
tangent vector
\[
\tilde V_{(s, p)}
= \left( \frac{\partial}{\partial s}\bigg|_s , \, V(s, p) \right)
\in T_s J \oplus T_p M .
\]
The first component selects the unit tangent vector to \(J\), and the second
component is the value of the time-dependent field at the pair \((s, p)\).
Smoothness of \(V\) as a map \(J \times M \to TM\) implies smoothness of
\(\tilde V\) as a section of \(T(J \times M)\), so \(\tilde V\) is a genuine smooth
autonomous vector field on \(J \times M\).
The Flow of the Lifted Field
Applying the
fundamental theorem on flows
to the autonomous field \(\tilde V\) on the manifold \(J \times M\) produces an
open subset \(\tilde{\mathcal{D}} \subseteq \mathbb{R} \times (J \times M)\) and a
smooth flow
\[
\tilde \theta : \tilde{\mathcal{D}} \to J \times M ,
\]
with the usual properties: \(\tilde \theta(0, (s, p)) = (s, p)\) for every initial
condition, and \(t \mapsto \tilde \theta(t, (s, p))\) is the maximal integral
curve of \(\tilde V\) starting at \((s, p)\). Write the components of
\(\tilde \theta\) explicitly as
\[
\tilde \theta\bigl(t, \, (s, p)\bigr)
= \bigl( \alpha(t, (s, p)), \, \beta(t, (s, p)) \bigr) ,
\]
where \(\alpha : \tilde{\mathcal{D}} \to J\) and
\(\beta : \tilde{\mathcal{D}} \to M\) are smooth.
Substituting the integral-curve equation \(\tilde \theta'(t) = \tilde V_{\tilde
\theta(t)}\) into the definition of \(\tilde V\) and reading off the two components
yields a coupled system for \(\alpha\) and \(\beta\):
\[
\frac{\partial \alpha}{\partial t}(t, (s, p)) = 1 ,
\qquad \alpha\bigl(0, (s, p)\bigr) = s ,
\]
\[
\frac{\partial \beta}{\partial t}(t, (s, p))
= V\bigl( \alpha(t, (s, p)), \, \beta(t, (s, p)) \bigr) ,
\qquad \beta\bigl(0, (s, p)\bigr) = p .
\]
The equation for \(\alpha\) is decoupled and integrates immediately to give
\(\alpha(t, (s, p)) = t + s\). Substituting this into the equation for \(\beta\)
yields
\[
\frac{\partial \beta}{\partial t}(t, (s, p))
= V\bigl( t + s, \, \beta(t, (s, p)) \bigr) .
\]
The reading of the result is geometric. The first component
\(\alpha(t, (s, p)) = t + s\) records that the time coordinate of an integral curve
of \(\tilde V\) advances at unit rate, so that the parameter \(t\) on the integral
curve is identified directly with the elapsed time. The second component \(\beta\)
is a curve in \(M\) whose velocity at parameter \(t\) is the value of the original
time-dependent field at the lifted time \(t + s\) and the current point
\(\beta(t, (s, p))\) — exactly the integral-curve condition for \(V\), starting
from \(p\) at time \(s\).
The Time-Dependent Flow Domain
The starting time of the integral curve in the lifted picture is the first
coordinate \(s\) of the initial condition \((s, p)\). Renaming this coordinate
\(t_0\) — the starting time of the time-dependent integral curve in the original
manifold \(M\) — and writing the elapsed-time parameter \(t\) of the lifted flow as
a current-time parameter via the relation \(t_{\text{current}} = t_0 + t\), the
parameter triple \((t_{\text{current}}, t_0, p)\) replaces the lifted parameters
\((t, (t_0, p)) \in \tilde{\mathcal{D}}\). The subset of
\(\mathbb{R} \times J \times M\) on which this reparametrization makes sense is
\[
\mathcal{E}
= \bigl\{ (t, t_0, p) \in \mathbb{R} \times J \times M
: (t - t_0, \, (t_0, p)) \in \tilde{\mathcal{D}} \bigr\} .
\]
Openness of \(\tilde{\mathcal{D}}\) in
\(\mathbb{R} \times (J \times M)\) and continuity of the map
\((t, t_0, p) \mapsto (t - t_0, (t_0, p))\) imply openness of \(\mathcal{E}\) in
\(\mathbb{R} \times J \times M\). Moreover, the membership
\((t - t_0, (t_0, p)) \in \tilde{\mathcal{D}} \subseteq \mathbb{R} \times
(J \times M)\) forces the initial-condition component to lie in \(J \times M\),
so \(t_0 \in J\) automatically; and the identity \(\alpha(t - t_0, (t_0, p)) =
(t - t_0) + t_0 = t\) — combined with the fact that \(\alpha\) takes values in
\(J\) — shows that whenever \((t, t_0, p) \in \mathcal{E}\) the current time
\(t\) lies in \(J\) as well. Hence
\[
\mathcal{E} \subseteq J \times J \times M ,
\]
and the time-dependent flow we are about to define will take \((t, t_0, p)\) with
\(t, t_0 \in J\). The construction of the flow itself, together with the statement
and proof of its defining properties, is the subject of the next section.
The Fundamental Theorem on Time-Dependent Flows
The lifted construction produces, by projection onto the second factor, the map
that plays the role of the flow in the time-dependent regime. Where the autonomous
flow is parametrized by a single time variable, the time-dependent flow is
parametrized by current time, starting time, and starting point: the trajectory
that passes through \(p\) at time \(t_0\) is a function of where we are and how
long we have been moving.
Definition: Time-Dependent Flow
Let \(V : J \times M \to TM\) be a smooth time-dependent vector field, and let
\(\tilde V\) be the lifted vector field on \(J \times M\) with flow
\(\tilde \theta = (\alpha, \beta) : \tilde{\mathcal{D}} \to J \times M\)
constructed in the previous section. The time-dependent flow of
\(V\) is the map
\[
\psi : \mathcal{E} \to M , \qquad
\psi(t, t_0, p) = \beta\bigl( t - t_0, \, (t_0, p) \bigr) ,
\]
where \(\mathcal{E} \subseteq J \times J \times M\) is the open subset defined
in the lifting construction.
Smoothness of \(\beta\) on \(\tilde{\mathcal{D}}\) and openness of \(\mathcal{E}\)
imply smoothness of \(\psi\) on \(\mathcal{E}\). The differential equation
satisfied by \(\beta\) translates directly into an equation for \(\psi\). For fixed
starting data \((t_0, p)\) and varying current time \(t\), set
\(\psi^{(t_0, p)}(t) = \psi(t, t_0, p) = \beta(t - t_0, (t_0, p))\). After the
renaming \(s = t_0\) of the lifting construction, the equation derived in the
previous section reads
\(\partial \beta / \partial \tau (\tau, (t_0, p)) = V(\tau + t_0, \beta(\tau,
(t_0, p)))\). Applying the chain rule to the composition
\(t \mapsto \beta(t - t_0, (t_0, p))\) — whose inner derivative is
\(d(t - t_0)/dt = 1\) — and substituting \(\tau = t - t_0\) gives
\[
\bigl( \psi^{(t_0, p)} \bigr)'(t)
= \frac{\partial \beta}{\partial \tau}(t - t_0, (t_0, p))
= V\bigl( (t - t_0) + t_0, \, \beta(t - t_0, (t_0, p)) \bigr)
= V\bigl( t, \, \psi^{(t_0, p)}(t) \bigr) ,
\]
with initial value \(\psi^{(t_0, p)}(t_0) = \beta(0, (t_0, p)) = p\), so
\(\psi^{(t_0, p)}\) is an integral curve of \(V\) with starting time \(t_0\) and
starting point \(p\). The defining properties of \(\psi\) are collected in the
theorem below.
The Fundamental Theorem
The autonomous fundamental theorem produced a maximal flow with three structural
properties: smoothness, the group law, and characterization of each trajectory as
a maximal integral curve. The time-dependent theorem records the corresponding
structural data, with the group law replaced by a two-parameter cocycle relation
that reflects the non-autonomous nature of the trajectories.
Theorem (Fundamental Theorem on Time-Dependent Flows)
Let \(M\) be a smooth manifold, let \(J \subseteq \mathbb{R}\) be an open
interval, and let \(V : J \times M \to TM\) be a smooth time-dependent vector
field on \(M\). There exist an open subset
\(\mathcal{E} \subseteq J \times J \times M\) and a smooth map
\(\psi : \mathcal{E} \to M\) — the time-dependent flow of \(V\) — with the
following properties.
-
(a) Existence and uniqueness of trajectories. For every
\(t_0 \in J\) and \(p \in M\), the set
\(\mathcal{E}^{(t_0, p)} = \{ t \in J : (t, t_0, p) \in \mathcal{E} \}\) is
an open interval containing \(t_0\), and the smooth curve
\(\psi^{(t_0, p)} : \mathcal{E}^{(t_0, p)} \to M\) defined by
\(\psi^{(t_0, p)}(t) = \psi(t, t_0, p)\) is the unique maximal integral
curve of \(V\) satisfying \(\psi^{(t_0, p)}(t_0) = p\).
-
(b) Reparametrization invariance. If
\(t_1 \in \mathcal{E}^{(t_0, p)}\) and \(q = \psi^{(t_0, p)}(t_1)\), then
\(\mathcal{E}^{(t_1, q)} = \mathcal{E}^{(t_0, p)}\) and
\(\psi^{(t_1, q)} = \psi^{(t_0, p)}\).
-
(c) Time-slice diffeomorphisms. For each \((t_1, t_0) \in
J \times J\), the set
\(M_{t_1, t_0} = \{ p \in M : (t_1, t_0, p) \in \mathcal{E} \}\) is open in
\(M\), and the map \(\psi_{t_1, t_0} : M_{t_1, t_0} \to M\) defined by
\(\psi_{t_1, t_0}(p) = \psi(t_1, t_0, p)\) is a diffeomorphism onto
\(M_{t_0, t_1}\), with inverse \(\psi_{t_0, t_1}\).
-
(d) Cocycle relation. Whenever \(p \in M_{t_1, t_0}\) and
\(\psi_{t_1, t_0}(p) \in M_{t_2, t_1}\), we have \(p \in M_{t_2, t_0}\) and
\[
\psi_{t_2, t_1} \circ \psi_{t_1, t_0}(p)
= \psi_{t_2, t_0}(p) .
\]
The cocycle relation in (d) is the two-parameter replacement for the group law
\(\theta_{t_2} \circ \theta_{t_1} = \theta_{t_1 + t_2}\) of an autonomous flow. The
autonomous flow can be recovered from the time-dependent flow when \(V\) is
constant in \(t\): the time-slice diffeomorphism \(\psi_{t_1, t_0}\) then depends
only on the difference \(t_1 - t_0\), and the cocycle relation collapses to the
additive group law in that difference.
Proof (outline):
Every assertion is read off from the corresponding property of the lifted flow
\(\tilde \theta = (\alpha, \beta)\) on \(J \times M\), which exists and is
smooth by the autonomous
fundamental theorem on flows
applied to the lifted field.
(a). The map \(t \mapsto \psi^{(t_0, p)}(t)\) is an integral
curve of \(V\) starting at \(p\) at time \(t_0\) by the differential equation
derived above. For uniqueness and maximality, suppose
\(\gamma : J_0 \to M\) is any integral curve of \(V\) with
\(\gamma(t_0) = p\), and define a lifted curve \(\tilde \gamma : J_0 \to
J \times M\) by \(\tilde \gamma(t) = (t, \gamma(t))\). The velocity of
\(\tilde \gamma\) at \(t\) is
\(\bigl(\partial / \partial s |_t, \, \gamma'(t)\bigr) =
\bigl(\partial / \partial s |_t, \, V(t, \gamma(t))\bigr) =
\tilde V_{\tilde \gamma(t)}\), so \(\tilde \gamma\) is an integral curve of
\(\tilde V\) starting at \((t_0, p)\) when \(t = t_0\). The shifted curve
\(t \mapsto \tilde \theta(t - t_0, (t_0, p))\) is also an integral curve of
\(\tilde V\) — its parameter \(s = t - t_0\) ranges through the lifted
maximal interval \(\tilde{\mathcal{D}}^{(t_0, p)}\) — and it too takes the
value \((t_0, p)\) at \(t = t_0\), since \(\tilde \theta(0, (t_0, p)) = (t_0,
p)\). Uniqueness and maximality of integral curves of \(\tilde V\) therefore
identify \(\tilde \gamma\) as the restriction of
\(t \mapsto \tilde \theta(t - t_0, (t_0, p))\) to \(J_0\), with
\(J_0 \subseteq t_0 + \tilde{\mathcal{D}}^{(t_0, p)} = \mathcal{E}^{(t_0,
p)}\). Projecting to the \(M\)-component gives \(\gamma\) as the restriction
of \(\psi^{(t_0, p)}\) to \(J_0\). The set \(\mathcal{E}^{(t_0, p)}\) is
therefore the maximal interval on which the integral curve through
\((t_0, p)\) exists, and is an open interval containing \(t_0\) since it is
the affine shift of an open interval in the lifted flow domain.
(b). Suppose \(t_1 \in \mathcal{E}^{(t_0, p)}\) and set
\(q = \psi^{(t_0, p)}(t_1)\). Both \(\psi^{(t_0, p)}\) and \(\psi^{(t_1, q)}\)
are integral curves of \(V\) that pass through \(q\) at time \(t_1\), so by the
uniqueness clause of (a) applied at time \(t_1\), they coincide on the
intersection of their domains, and by maximality they have the same domain.
(d). Suppose \(p \in M_{t_1, t_0}\) and
\(\psi_{t_1, t_0}(p) \in M_{t_2, t_1}\), and set \(q = \psi_{t_1, t_0}(p) =
\psi^{(t_0, p)}(t_1)\). Part (b) gives
\(\psi^{(t_1, q)}(t_2) = \psi^{(t_0, p)}(t_2)\), which unwinds to
\(\psi_{t_2, t_1} \circ \psi_{t_1, t_0}(p) = \psi_{t_2, t_0}(p)\); in
particular \(t_2 \in \mathcal{E}^{(t_0, p)}\), so
\(p \in M_{t_2, t_0}\).
(c). Openness of \(M_{t_1, t_0}\) in \(M\) follows from
openness of \(\mathcal{E}\). The containment
\(\psi_{t_1, t_0}(M_{t_1, t_0}) \subseteq M_{t_0, t_1}\) is part (b) read as a
statement on domains: for \(p \in M_{t_1, t_0}\) and
\(q = \psi_{t_1, t_0}(p)\), part (b) gives
\(\mathcal{E}^{(t_1, q)} = \mathcal{E}^{(t_0, p)}\), so
\(t_0 \in \mathcal{E}^{(t_1, q)}\), which is exactly
\(q \in M_{t_0, t_1}\). Reversing the roles of \(t_0\) and \(t_1\) gives the
opposite containment
\(\psi_{t_0, t_1}(M_{t_0, t_1}) \subseteq M_{t_1, t_0}\), and the cocycle
relation (d) applied with \(t_2 = t_0\) yields
\(\psi_{t_0, t_1} \circ \psi_{t_1, t_0} = \psi_{t_0, t_0} = \mathrm{id}\) on
\(M_{t_1, t_0}\) — using that \(\psi_{t_0, t_0}(p) = \psi^{(t_0, p)}(t_0) = p\)
by (a). Together these identify \(\psi_{t_1, t_0}\) as a smooth bijection
\(M_{t_1, t_0} \to M_{t_0, t_1}\) with smooth inverse \(\psi_{t_0, t_1}\),
which is the definition of a diffeomorphism.
The order in which the four parts are proved — (a), (b), (d), (c), rather than the
order in which they are stated — reflects their logical dependence. Existence and
uniqueness in (a) is the input that makes the reparametrization identity in (b)
available; the cocycle relation in (d) is a corollary of (b) read at the level of
time-slice maps; and the diffeomorphism property in (c) is then a consequence of
(b) and (d) combined. The interlocking of the four properties is exactly what
makes the time-dependent flow a coherent geometric object rather than a loose
collection of integral curves.
Flow Matching and the Time-Dependent Flow
A modern application in which the time-dependent flow plays an essential role lies
outside classical mechanics or control theory altogether: in the construction of
generative models in machine learning. The strategy is to learn a time-dependent
velocity field whose flow transports a base probability distribution onto a target
one — the distribution of natural images, of natural-language text, or of any
other complex data type — and to sample from the target by integrating the learned
field from a sample of the base distribution. The theorem of the previous section
is precisely the rigorous foundation on which this construction rests: it
guarantees that the learned velocity field generates a well-defined two-parameter
family of diffeomorphisms whose time-slice map can be evaluated by ODE integration,
and that the cocycle relation ensures consistency when these integrations are
composed.
The Time-Dependent Flow in Generative Modeling
In the
flow-matching
construction
for generative models, a neural network is trained to approximate a
time-dependent velocity field \(\mathbf{u}_t(\mathbf{x})\) on
\(J \times \mathbb{R}^n\) — a time-dependent vector field in the sense of this
page, with \(J = (0, 1)\). The generative procedure is the time-slice
diffeomorphism \(\psi_{1, 0}\) of the corresponding time-dependent flow: a
sample \(\mathbf{x}_0\) drawn from the base distribution at time \(0\) is
carried by the flow to \(\psi_{1, 0}(\mathbf{x}_0)\), a sample from the target
distribution at time \(1\). The well-posedness of this procedure was
previously available through the
Lipschitz
version of the Picard–Lindelöf theorem on Euclidean space, which gives
existence and uniqueness of integral curves for each fixed sample but treats
each trajectory in isolation. The fundamental theorem of this page does more:
it produces \(\psi_{1, 0}\) as a single smooth diffeomorphism between open
subsets of the data manifold, with composition behavior governed by the
cocycle relation. The diffeomorphism property underlies the change-of-variables
formula for probability densities under the flow, the cocycle relation
underlies the consistency of partial integrations, and the smoothness of
\(\psi\) jointly in current time, starting time, and starting point —
combined with the standard smooth dependence of solutions to ODEs on
parameters — underlies the differentiability of the generative procedure
with respect to the parameters of the learned field, which is the property
exploited by backpropagation through the flow during training.
The connection runs in both directions, and the geometric content of the
time-dependent flow has a corresponding payoff outside Euclidean settings. When
the data are naturally modeled as a manifold rather than as
\(\mathbb{R}^n\) — for example, when the data are rotations, points on a sphere,
or probability distributions over a finite set — the Euclidean Picard–Lindelöf
statement no longer applies directly, but the manifold-level fundamental theorem of
this page does. The same flow-matching construction can therefore be transferred
to manifold-valued data, with the time-dependent flow producing a diffeomorphism
between open subsets of the data manifold by exactly the same mechanism. The
technical apparatus developed here is, in this sense, the manifold-level
foundation that flow-based generative modeling needs in order to make the leap from
Euclidean to geometric data — and it is the structural endpoint of the present
chapter, the place where the theory of flows on smooth manifolds becomes the
apparatus on which a current branch of machine learning runs.