Oscillator: Use adaptive black-box time stepper and dense output. (#500)

* Allow to use adaptive black-box time stepper and dense output via option -ts RadauIIA
* Keep support for time stepping without adaptivity and dense output (default without -ts option stays generalized alpha)

---------

Co-authored-by: Gerasimos Chourdakis <chourdak@in.tum.de>

Author: BenjaminRodenberg

changelog-entries/500.md

@@ -0,0 +1 @@
+- Offer adaptive black-box time-stepping with RadauIIA scheme in oscillator case. Demonstrates use of time interpolation and dense output.

oscillator/solver-python/oscillator.py

@@ -133,22 +133,36 @@ class Participant(Enum):
     precice_dt = participant.get_max_time_step_size()
     dt = np.min([precice_dt, my_dt])
 
-    read_times = time_stepper.rhs_eval_points(dt)
-    f = len(read_times) * [None]
-
-    for i in range(len(read_times)):
-        read_data = participant.read_data(mesh_name, read_data_name, vertex_ids, read_times[i])
-        f[i] = read_data[0]
-
-    # do generalized alpha step
-    u_new, v_new, a_new = time_stepper.do_step(u, v, a, f, dt)
-    t_new = t + dt
-
-    write_data = [connecting_spring.k * u_new]
-
-    participant.write_data(mesh_name, write_data_name, vertex_ids, write_data)
-
-    participant.advance(dt)
+    def f(t): return participant.read_data(mesh_name, read_data_name, vertex_ids, t)[0]
+
+    # do time step, write data, and advance
+    # performs adaptive time stepping with dense output; multiple write calls per time step
+    if args.time_stepping == timeSteppers.TimeSteppingSchemes.Radau_IIA.value:
+        u_new, v_new, a_new, sol = time_stepper.do_step(u, v, a, f, dt)
+        t_new = t + dt
+        # create n samples_per_step of time stepping scheme. Degree of dense
+        # interpolating function is usually larger 1 and, therefore, we need
+        # multiple samples per step.
+        samples_per_step = 5
+        n_time_steps = len(sol.ts)  # number of time steps performed by adaptive time stepper
+        n_pseudo = samples_per_step * n_time_steps  # samples_per_step times no. time steps per window.
+
+        t_pseudo = 0
+        for i in range(n_pseudo):
+            # perform n_pseudo pseudosteps
+            dt_pseudo = dt / n_pseudo
+            t_pseudo += dt_pseudo
+            write_data = [connecting_spring.k * sol(t_pseudo)[0]]
+            participant.write_data(mesh_name, write_data_name, vertex_ids, write_data)
+            participant.advance(dt_pseudo)
+
+    else:  # simple time stepping without dense output; only a single write call per time step
+        u_new, v_new, a_new = time_stepper.do_step(u, v, a, f, dt)
+        t_new = t + dt
+
+        write_data = [connecting_spring.k * u_new]
+        participant.write_data(mesh_name, write_data_name, vertex_ids, write_data)
+        participant.advance(dt)
 
     if participant.requires_reading_checkpoint():
         u = u_cp

oscillator/solver-python/timeSteppers.py

@@ -30,12 +30,8 @@ def __init__(self, stiffness, mass, alpha_f=0.4, alpha_m=0.2) -> None:
         self.stiffness = stiffness
         self.mass = mass
 
-    def rhs_eval_points(self, dt) -> List[float]:
-        return [(1 - self.alpha_f) * dt]
-
-    def do_step(self, u, v, a, f, dt) -> Tuple[float, float, float]:
-        if isinstance(f, list):  # if f is list, turn it into a number
-            f = f[0]
+    def do_step(self, u, v, a, rhs, dt) -> Tuple[float, float, float]:
+        f = rhs((1 - self.alpha_f) * dt)
 
         m = 3 * [None]
         m[0] = (1 - self.alpha_m) / (self.beta * dt**2)
@@ -71,31 +67,25 @@ def __init__(self, ode_system) -> None:
         self.ode_system = ode_system
         pass
 
-    def rhs_eval_points(self, dt) -> List[float]:
-        return [self.c[0] * dt, self.c[1] * dt, self.c[2] * dt, self.c[3] * dt]
-
-    def do_step(self, u, v, a, f, dt) -> Tuple[float, float, float]:
+    def do_step(self, u, v, a, rhs, dt) -> Tuple[float, float, float]:
         assert (isinstance(u, type(v)))
 
         n_stages = 4
 
-        if isinstance(f, numbers.Number):  # if f is number, assume constant f
-            f = n_stages * [f]
-
         if isinstance(u, np.ndarray):
             x = np.concatenate([u, v])
-            rhs = [np.concatenate([np.array([0, 0]), f[i]]) for i in range(n_stages)]
+            def f(t): return np.concatenate([np.array([0, 0]), rhs(t)])
         elif isinstance(u, numbers.Number):
             x = np.array([u, v])
-            rhs = [np.array([0, f[i]]) for i in range(n_stages)]
+            def f(t): return np.array([0, rhs(t)])
         else:
             raise Exception(f"Cannot handle input type {type(u)} of u and v")
 
         s = n_stages * [None]
-        s[0] = self.ode_system.dot(x) + rhs[0]
-        s[1] = self.ode_system.dot(x + self.a[1, 0] * s[0] * dt) + rhs[1]
-        s[2] = self.ode_system.dot(x + self.a[2, 1] * s[1] * dt) + rhs[2]
-        s[3] = self.ode_system.dot(x + self.a[3, 2] * s[2] * dt) + rhs[3]
+        s[0] = self.ode_system.dot(x) + f(self.c[0] * dt)
+        s[1] = self.ode_system.dot(x + self.a[1, 0] * s[0] * dt) + f(self.c[1] * dt)
+        s[2] = self.ode_system.dot(x + self.a[2, 1] * s[1] * dt) + f(self.c[2] * dt)
+        s[3] = self.ode_system.dot(x + self.a[3, 2] * s[2] * dt) + f(self.c[3] * dt)
 
         x_new = x
 
@@ -119,14 +109,7 @@ def __init__(self, ode_system) -> None:
         self.ode_system = ode_system
         pass
 
-    def rhs_eval_points(self, dt) -> List[float]:
-        return np.linspace(0, dt, 5)  # will create an interpolant from this later
-
-    def do_step(self, u, v, a, f, dt) -> Tuple[float, float, float]:
-        from brot.interpolation import do_lagrange_interpolation
-
-        ts = self.rhs_eval_points(dt)
-
+    def do_step(self, u, v, a, rhs, dt) -> Tuple[float, float, float]:
         t0 = 0
 
         assert (isinstance(u, type(v)))
@@ -135,25 +118,24 @@ def do_step(self, u, v, a, f, dt) -> Tuple[float, float, float]:
             x0 = np.concatenate([u, v])
             f = np.array(f)
             assert (u.shape[0] == f.shape[1])
-            def rhs_fun(t, x): return np.concatenate([np.array([np.zeros_like(t), np.zeros_like(t)]), [
-                do_lagrange_interpolation(t, ts, f[:, i]) for i in range(u.shape[0])]])
+            def rhs_fun(t): return np.concatenate([np.array([np.zeros_like(t), np.zeros_like(t)]), rhs(t)])
         elif isinstance(u, numbers.Number):
             x0 = np.array([u, v])
-            def rhs_fun(t, x): return np.array([np.zeros_like(t), do_lagrange_interpolation(t, ts, f)])
+            def rhs_fun(t): return np.array([np.zeros_like(t), rhs(t)])
         else:
             raise Exception(f"Cannot handle input type {type(u)} of u and v")
 
         def fun(t, x):
-            return self.ode_system.dot(x) + rhs_fun(t, x)
+            return self.ode_system.dot(x) + rhs_fun(t)
 
-        # use large rtol and atol to circumvent error control.
+        # use adaptive time stepping; dense_output=True allows us to sample from continuous function later
         ret = sp.integrate.solve_ivp(fun, [t0, t0 + dt], x0, method="Radau",
-                                     first_step=dt, max_step=dt, rtol=10e10, atol=10e10)
+                                     dense_output=True, rtol=10e-5, atol=10e-9)
 
         a_new = None
         if isinstance(u, np.ndarray):
             u_new, v_new = ret.y[0:2, -1], ret.y[2:4, -1]
         elif isinstance(u, numbers.Number):
             u_new, v_new = ret.y[:, -1]
 
-        return u_new, v_new, a_new
+        return u_new, v_new, a_new, ret.sol